Overview

Dataset statistics

Number of variables117
Number of observations4000
Missing cells121562
Missing cells (%)26.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.6 MiB
Average record size in memory936.0 B

Variable types

NUM111
BOOL4
CAT2

Warnings

MechVent_min has constant value "4000" Constant
Weight_max is highly correlated with Weight_min and 1 other fieldsHigh correlation
Weight_min is highly correlated with Weight_max and 1 other fieldsHigh correlation
Weight_med is highly correlated with Weight_min and 1 other fieldsHigh correlation
NIMAP_max is highly correlated with NIDiasABP_maxHigh correlation
NIDiasABP_max is highly correlated with NIMAP_maxHigh correlation
BUN_max is highly correlated with BUN_min and 1 other fieldsHigh correlation
BUN_min is highly correlated with BUN_max and 1 other fieldsHigh correlation
BUN_med is highly correlated with BUN_min and 1 other fieldsHigh correlation
Creatinine_max is highly correlated with Creatinine_min and 1 other fieldsHigh correlation
Creatinine_min is highly correlated with Creatinine_max and 1 other fieldsHigh correlation
Creatinine_med is highly correlated with Creatinine_min and 1 other fieldsHigh correlation
HCO3_med is highly correlated with HCO3_min and 1 other fieldsHigh correlation
HCO3_min is highly correlated with HCO3_medHigh correlation
HCO3_max is highly correlated with HCO3_medHigh correlation
Platelets_max is highly correlated with Platelets_min and 1 other fieldsHigh correlation
Platelets_min is highly correlated with Platelets_max and 1 other fieldsHigh correlation
Platelets_med is highly correlated with Platelets_min and 1 other fieldsHigh correlation
Na_med is highly correlated with Na_maxHigh correlation
Na_max is highly correlated with Na_medHigh correlation
WBC_med is highly correlated with WBC_min and 1 other fieldsHigh correlation
WBC_min is highly correlated with WBC_medHigh correlation
WBC_max is highly correlated with WBC_medHigh correlation
SaO2_med is highly correlated with SaO2_maxHigh correlation
SaO2_max is highly correlated with SaO2_medHigh correlation
Albumin_max is highly correlated with Albumin_min and 1 other fieldsHigh correlation
Albumin_min is highly correlated with Albumin_max and 1 other fieldsHigh correlation
Albumin_med is highly correlated with Albumin_min and 1 other fieldsHigh correlation
ALP_med is highly correlated with ALP_min and 1 other fieldsHigh correlation
ALP_min is highly correlated with ALP_medHigh correlation
ALP_max is highly correlated with ALP_medHigh correlation
ALT_med is highly correlated with ALT_maxHigh correlation
ALT_max is highly correlated with ALT_medHigh correlation
AST_med is highly correlated with AST_maxHigh correlation
AST_max is highly correlated with AST_medHigh correlation
Bilirubin_max is highly correlated with Bilirubin_min and 1 other fieldsHigh correlation
Bilirubin_min is highly correlated with Bilirubin_max and 1 other fieldsHigh correlation
Bilirubin_med is highly correlated with Bilirubin_min and 1 other fieldsHigh correlation
Cholesterol_max is highly correlated with Cholesterol_min and 1 other fieldsHigh correlation
Cholesterol_min is highly correlated with Cholesterol_max and 1 other fieldsHigh correlation
Cholesterol_med is highly correlated with Cholesterol_min and 1 other fieldsHigh correlation
TroponinI_med is highly correlated with TroponinI_min and 1 other fieldsHigh correlation
TroponinI_min is highly correlated with TroponinI_medHigh correlation
TroponinI_max is highly correlated with TroponinI_medHigh correlation
TroponinT_med is highly correlated with TroponinT_min and 1 other fieldsHigh correlation
TroponinT_min is highly correlated with TroponinT_medHigh correlation
TroponinT_max is highly correlated with TroponinT_medHigh correlation
Height has 1910 (47.8%) missing values Missing
Weight_min has 296 (7.4%) missing values Missing
Weight_max has 296 (7.4%) missing values Missing
Weight_med has 296 (7.4%) missing values Missing
GCS_min has 64 (1.6%) missing values Missing
GCS_max has 64 (1.6%) missing values Missing
GCS_med has 64 (1.6%) missing values Missing
HR_min has 63 (1.6%) missing values Missing
HR_max has 63 (1.6%) missing values Missing
HR_med has 63 (1.6%) missing values Missing
NIDiasABP_min has 518 (12.9%) missing values Missing
NIDiasABP_max has 518 (12.9%) missing values Missing
NIDiasABP_med has 518 (12.9%) missing values Missing
NIMAP_min has 520 (13.0%) missing values Missing
NIMAP_max has 520 (13.0%) missing values Missing
NIMAP_med has 520 (13.0%) missing values Missing
NISysABP_min has 515 (12.9%) missing values Missing
NISysABP_max has 515 (12.9%) missing values Missing
NISysABP_med has 515 (12.9%) missing values Missing
RespRate_min has 2899 (72.5%) missing values Missing
RespRate_max has 2899 (72.5%) missing values Missing
RespRate_med has 2899 (72.5%) missing values Missing
Temp_min has 64 (1.6%) missing values Missing
Temp_max has 64 (1.6%) missing values Missing
Temp_med has 64 (1.6%) missing values Missing
Urine_min has 117 (2.9%) missing values Missing
Urine_max has 117 (2.9%) missing values Missing
Urine_med has 117 (2.9%) missing values Missing
HCT_min has 64 (1.6%) missing values Missing
HCT_max has 64 (1.6%) missing values Missing
HCT_med has 64 (1.6%) missing values Missing
BUN_min has 64 (1.6%) missing values Missing
BUN_max has 64 (1.6%) missing values Missing
BUN_med has 64 (1.6%) missing values Missing
Creatinine_min has 64 (1.6%) missing values Missing
Creatinine_max has 64 (1.6%) missing values Missing
Creatinine_med has 64 (1.6%) missing values Missing
Glucose_min has 113 (2.8%) missing values Missing
Glucose_max has 113 (2.8%) missing values Missing
Glucose_med has 113 (2.8%) missing values Missing
HCO3_min has 76 (1.9%) missing values Missing
HCO3_max has 76 (1.9%) missing values Missing
HCO3_med has 76 (1.9%) missing values Missing
Mg_min has 103 (2.6%) missing values Missing
Mg_max has 103 (2.6%) missing values Missing
Mg_med has 103 (2.6%) missing values Missing
Platelets_min has 68 (1.7%) missing values Missing
Platelets_max has 68 (1.7%) missing values Missing
Platelets_med has 68 (1.7%) missing values Missing
K_min has 96 (2.4%) missing values Missing
K_max has 96 (2.4%) missing values Missing
K_med has 96 (2.4%) missing values Missing
Na_min has 75 (1.9%) missing values Missing
Na_max has 75 (1.9%) missing values Missing
Na_med has 75 (1.9%) missing values Missing
WBC_min has 73 (1.8%) missing values Missing
WBC_max has 73 (1.8%) missing values Missing
WBC_med has 73 (1.8%) missing values Missing
pH_min has 962 (24.1%) missing values Missing
pH_max has 962 (24.1%) missing values Missing
pH_med has 962 (24.1%) missing values Missing
PaCO2_min has 977 (24.4%) missing values Missing
PaCO2_max has 977 (24.4%) missing values Missing
PaCO2_med has 977 (24.4%) missing values Missing
PaO2_min has 977 (24.4%) missing values Missing
PaO2_max has 977 (24.4%) missing values Missing
PaO2_med has 977 (24.4%) missing values Missing
DiasABP_min has 1221 (30.5%) missing values Missing
DiasABP_max has 1221 (30.5%) missing values Missing
DiasABP_med has 1221 (30.5%) missing values Missing
FiO2_min has 1283 (32.1%) missing values Missing
FiO2_max has 1283 (32.1%) missing values Missing
FiO2_med has 1283 (32.1%) missing values Missing
MAP_min has 1208 (30.2%) missing values Missing
MAP_max has 1208 (30.2%) missing values Missing
MAP_med has 1208 (30.2%) missing values Missing
SysABP_min has 1220 (30.5%) missing values Missing
SysABP_max has 1220 (30.5%) missing values Missing
SysABP_med has 1220 (30.5%) missing values Missing
SaO2_min has 2208 (55.2%) missing values Missing
SaO2_max has 2208 (55.2%) missing values Missing
SaO2_med has 2208 (55.2%) missing values Missing
Albumin_min has 2385 (59.6%) missing values Missing
Albumin_max has 2385 (59.6%) missing values Missing
Albumin_med has 2385 (59.6%) missing values Missing
ALP_min has 2310 (57.8%) missing values Missing
ALP_max has 2310 (57.8%) missing values Missing
ALP_med has 2310 (57.8%) missing values Missing
ALT_min has 2279 (57.0%) missing values Missing
ALT_max has 2279 (57.0%) missing values Missing
ALT_med has 2279 (57.0%) missing values Missing
AST_min has 2275 (56.9%) missing values Missing
AST_max has 2275 (56.9%) missing values Missing
AST_med has 2275 (56.9%) missing values Missing
Bilirubin_min has 2282 (57.0%) missing values Missing
Bilirubin_max has 2282 (57.0%) missing values Missing
Bilirubin_med has 2282 (57.0%) missing values Missing
Lactate_min has 1817 (45.4%) missing values Missing
Lactate_max has 1817 (45.4%) missing values Missing
Lactate_med has 1817 (45.4%) missing values Missing
Cholesterol_min has 3695 (92.4%) missing values Missing
Cholesterol_max has 3695 (92.4%) missing values Missing
Cholesterol_med has 3695 (92.4%) missing values Missing
TroponinI_min has 3795 (94.9%) missing values Missing
TroponinI_max has 3795 (94.9%) missing values Missing
TroponinI_med has 3795 (94.9%) missing values Missing
TroponinT_min has 3137 (78.4%) missing values Missing
TroponinT_max has 3137 (78.4%) missing values Missing
TroponinT_med has 3137 (78.4%) missing values Missing
PATIENT_ID has unique values Unique
Urine_min has 664 (16.6%) zeros Zeros
Urine_med has 46 (1.1%) zeros Zeros

Reproduction

Analysis started2020-09-17 13:51:35.167826
Analysis finished2020-09-17 13:52:48.823369
Duration1 minute and 13.66 seconds
Software versionpandas-profiling v2.9.0
Download configurationconfig.yaml

Variables

PATIENT_ID
Real number (ℝ≥0)

UNIQUE

Distinct4000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean137605.122
Minimum132539
Maximum142673
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:52:48.937945image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum132539
5-th percentile133038.95
Q1135075.75
median137592.5
Q3140100.25
95-th percentile142176.2
Maximum142673
Range10134
Interquartile range (IQR)5024.5

Descriptive statistics

Standard deviation2923.608886
Coefficient of variation (CV)0.02124636673
Kurtosis-1.191489871
Mean137605.122
Median Absolute Deviation (MAD)2513
Skewness0.00584744361
Sum550420488
Variance8547488.92
MonotocityStrictly increasing
2020-09-17T09:52:49.178036image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1351621< 0.1%
 
1398891< 0.1%
 
1422051< 0.1%
 
1419601< 0.1%
 
1337641< 0.1%
 
1358111< 0.1%
 
1399051< 0.1%
 
1421061< 0.1%
 
1358031< 0.1%
 
1378501< 0.1%
 
Other values (3990)399099.8%
 
ValueCountFrequency (%) 
1325391< 0.1%
 
1325401< 0.1%
 
1325411< 0.1%
 
1325431< 0.1%
 
1325451< 0.1%
 
ValueCountFrequency (%) 
1426731< 0.1%
 
1426711< 0.1%
 
1426701< 0.1%
 
1426671< 0.1%
 
1426651< 0.1%
 

ihd
Boolean

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
0
3446 
1
554 
ValueCountFrequency (%) 
0344686.2%
 
155413.9%
 
2020-09-17T09:52:49.341363image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Age
Real number (ℝ≥0)

Distinct76
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.2475
Minimum15
Maximum90
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:52:49.500215image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile29
Q152.75
median67
Q378
95-th percentile89
Maximum90
Range75
Interquartile range (IQR)25.25

Descriptive statistics

Standard deviation17.56094646
Coefficient of variation (CV)0.2733327594
Kurtosis-0.3325881438
Mean64.2475
Median Absolute Deviation (MAD)12
Skewness-0.6062680114
Sum256990
Variance308.3868405
MonotocityNot monotonic
2020-09-17T09:52:49.736876image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
901644.1%
 
771263.1%
 
781042.6%
 
831022.5%
 
791002.5%
 
74932.3%
 
72902.2%
 
80892.2%
 
73882.2%
 
81862.1%
 
Other values (66)295874.0%
 
ValueCountFrequency (%) 
151< 0.1%
 
161< 0.1%
 
1740.1%
 
18120.3%
 
19180.4%
 
ValueCountFrequency (%) 
901644.1%
 
89411.0%
 
88511.3%
 
87491.2%
 
86651.6%
 

Gender
Categorical

Distinct2
Distinct (%)0.1%
Missing3
Missing (%)0.1%
Memory size31.2 KiB
male
2246 
female
1751 
ValueCountFrequency (%) 
male224656.1%
 
female175143.8%
 
(Missing)30.1%
 
2020-09-17T09:52:49.987267image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-17T09:52:50.109226image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T09:52:50.254790image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length6
Median length4
Mean length4.87475
Min length3

Height
Real number (ℝ≥0)

MISSING

Distinct57
Distinct (%)2.7%
Missing1910
Missing (%)47.8%
Infinite0
Infinite (%)0.0%
Mean169.6778947
Minimum121.9
Maximum205.7
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:52:50.473232image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum121.9
5-th percentile152.4
Q1162.6
median170.2
Q3177.8
95-th percentile185.4
Maximum205.7
Range83.8
Interquartile range (IQR)15.2

Descriptive statistics

Standard deviation10.8649074
Coefficient of variation (CV)0.06403254484
Kurtosis0.0004934701394
Mean169.6778947
Median Absolute Deviation (MAD)7.6
Skewness-0.245786223
Sum354626.8
Variance118.0462129
MonotocityNot monotonic
2020-09-17T09:52:50.717181image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
177.82135.3%
 
182.91884.7%
 
170.21734.3%
 
172.71694.2%
 
167.61624.0%
 
162.61533.8%
 
157.51423.5%
 
175.31373.4%
 
165.11243.1%
 
180.31152.9%
 
Other values (47)51412.8%
 
(Missing)191047.8%
 
ValueCountFrequency (%) 
121.920.1%
 
1271< 0.1%
 
129.51< 0.1%
 
132.11< 0.1%
 
134.620.1%
 
ValueCountFrequency (%) 
205.71< 0.1%
 
203.21< 0.1%
 
2031< 0.1%
 
200.720.1%
 
198.140.1%
 

ICUType
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
Medical ICU
1481 
Surgical ICU
1068 
Cardiac Surgery Recovery Unit
874 
Coronary Care Unit
577 
ValueCountFrequency (%) 
Medical ICU148137.0%
 
Surgical ICU106826.7%
 
Cardiac Surgery Recovery Unit87421.9%
 
Coronary Care Unit57714.4%
 
2020-09-17T09:52:50.953060image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Frequencies of value counts

Unique

Unique0 ?
Unique (%)0.0%
2020-09-17T09:52:51.077241image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T09:52:51.290488image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length29
Median length12
Mean length16.20975
Min length11

Weight_min
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct843
Distinct (%)22.8%
Missing296
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean81.08357181
Minimum21.7
Maximum300
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:52:51.498998image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum21.7
5-th percentile50.315
Q165.7
median78.15
Q391.625
95-th percentile121
Maximum300
Range278.3
Interquartile range (IQR)25.925

Descriptive statistics

Standard deviation23.2898023
Coefficient of variation (CV)0.2872320716
Kurtosis7.412608976
Mean81.08357181
Median Absolute Deviation (MAD)13.05
Skewness1.67008094
Sum300333.55
Variance542.4148913
MonotocityNot monotonic
2020-09-17T09:52:51.718514image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
701022.5%
 
80812.0%
 
90621.6%
 
60591.5%
 
75541.4%
 
65531.3%
 
100451.1%
 
85411.0%
 
73290.7%
 
82280.7%
 
Other values (833)315078.8%
 
(Missing)2967.4%
 
ValueCountFrequency (%) 
21.71< 0.1%
 
31.71< 0.1%
 
321< 0.1%
 
34.61< 0.1%
 
351< 0.1%
 
ValueCountFrequency (%) 
3001< 0.1%
 
2801< 0.1%
 
2531< 0.1%
 
2351< 0.1%
 
2301< 0.1%
 

Weight_max
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct881
Distinct (%)23.8%
Missing296
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean84.04357991
Minimum31.7
Maximum300
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:52:51.952414image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum31.7
5-th percentile52.3
Q167.7
median81
Q396
95-th percentile126
Maximum300
Range268.3
Interquartile range (IQR)28.3

Descriptive statistics

Standard deviation24.27108491
Coefficient of variation (CV)0.2887916594
Kurtosis6.617695515
Mean84.04357991
Median Absolute Deviation (MAD)14
Skewness1.607964625
Sum311297.42
Variance589.0855627
MonotocityNot monotonic
2020-09-17T09:52:52.211389image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
70711.8%
 
90561.4%
 
80551.4%
 
65491.2%
 
60441.1%
 
100401.0%
 
85391.0%
 
75380.9%
 
77290.7%
 
73280.7%
 
Other values (871)325581.4%
 
(Missing)2967.4%
 
ValueCountFrequency (%) 
31.71< 0.1%
 
321< 0.1%
 
34.61< 0.1%
 
351< 0.1%
 
361< 0.1%
 
ValueCountFrequency (%) 
3001< 0.1%
 
2801< 0.1%
 
2531< 0.1%
 
2351< 0.1%
 
2301< 0.1%
 

Weight_med
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct876
Distinct (%)23.7%
Missing296
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean83.24340173
Minimum21.7
Maximum300
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:52:52.460159image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum21.7
5-th percentile51.8
Q167
median80
Q395
95-th percentile124.485
Maximum300
Range278.3
Interquartile range (IQR)28

Descriptive statistics

Standard deviation23.96331452
Coefficient of variation (CV)0.2878704381
Kurtosis6.624566592
Mean83.24340173
Median Absolute Deviation (MAD)14
Skewness1.582974286
Sum308333.56
Variance574.2404428
MonotocityNot monotonic
2020-09-17T09:52:52.689701image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
70781.9%
 
80591.5%
 
90511.3%
 
60461.1%
 
65461.1%
 
75401.0%
 
100380.9%
 
85360.9%
 
77300.8%
 
73280.7%
 
Other values (866)325281.3%
 
(Missing)2967.4%
 
ValueCountFrequency (%) 
21.71< 0.1%
 
31.71< 0.1%
 
321< 0.1%
 
34.61< 0.1%
 
351< 0.1%
 
ValueCountFrequency (%) 
3001< 0.1%
 
2801< 0.1%
 
2531< 0.1%
 
2351< 0.1%
 
2301< 0.1%
 

GCS_min
Real number (ℝ≥0)

MISSING

Distinct13
Distinct (%)0.3%
Missing64
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean8.222560976
Minimum3
Maximum15
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:52:52.903839image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q13
median7
Q314
95-th percentile15
Maximum15
Range12
Interquartile range (IQR)11

Descriptive statistics

Standard deviation4.805519819
Coefficient of variation (CV)0.5844310347
Kurtosis-1.545992224
Mean8.222560976
Median Absolute Deviation (MAD)4
Skewness0.2502575264
Sum32364
Variance23.09302073
MonotocityNot monotonic
2020-09-17T09:52:53.106127image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%) 
3137834.4%
 
1571918.0%
 
143518.8%
 
62947.3%
 
72827.0%
 
81954.9%
 
101934.8%
 
131453.6%
 
91393.5%
 
111102.8%
 
Other values (3)1303.2%
 
(Missing)641.6%
 
ValueCountFrequency (%) 
3137834.4%
 
4360.9%
 
5451.1%
 
62947.3%
 
72827.0%
 
ValueCountFrequency (%) 
1571918.0%
 
143518.8%
 
131453.6%
 
12491.2%
 
111102.8%
 

GCS_max
Real number (ℝ≥0)

MISSING

Distinct13
Distinct (%)0.3%
Missing64
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean13.52820122
Minimum3
Maximum15
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:52:53.703811image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile8
Q112
median15
Q315
95-th percentile15
Maximum15
Range12
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.596492095
Coefficient of variation (CV)0.1919318062
Kurtosis2.38686935
Mean13.52820122
Median Absolute Deviation (MAD)0
Skewness-1.747218889
Sum53247
Variance6.741771198
MonotocityNot monotonic
2020-09-17T09:52:53.891971image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%) 
15270567.6%
 
113378.4%
 
102666.7%
 
141985.0%
 
91092.7%
 
8822.1%
 
7661.7%
 
13461.1%
 
6411.0%
 
12330.8%
 
Other values (3)531.3%
 
(Missing)641.6%
 
ValueCountFrequency (%) 
3280.7%
 
4150.4%
 
5100.2%
 
6411.0%
 
7661.7%
 
ValueCountFrequency (%) 
15270567.6%
 
141985.0%
 
13461.1%
 
12330.8%
 
113378.4%
 

GCS_med
Real number (ℝ≥0)

MISSING

Distinct25
Distinct (%)0.6%
Missing64
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean11.99580793
Minimum3
Maximum15
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:52:54.078341image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q110
median14
Q315
95-th percentile15
Maximum15
Range12
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.579082277
Coefficient of variation (CV)0.2983610857
Kurtosis-0.3079868296
Mean11.99580793
Median Absolute Deviation (MAD)1
Skewness-0.9017996621
Sum47215.5
Variance12.80982994
MonotocityNot monotonic
2020-09-17T09:52:54.288086image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%) 
15181345.3%
 
103899.7%
 
112396.0%
 
142325.8%
 
81904.8%
 
71814.5%
 
91674.2%
 
31553.9%
 
61323.3%
 
131203.0%
 
Other values (15)3188.0%
 
(Missing)641.6%
 
ValueCountFrequency (%) 
31553.9%
 
3.560.1%
 
4230.6%
 
4.540.1%
 
5210.5%
 
ValueCountFrequency (%) 
15181345.3%
 
14.5330.8%
 
142325.8%
 
13.5200.5%
 
131203.0%
 

HR_min
Real number (ℝ≥0)

MISSING

Distinct96
Distinct (%)2.4%
Missing63
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean68.62334265
Minimum3.5
Maximum115
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:52:54.518097image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum3.5
5-th percentile47
Q160
median68
Q377
95-th percentile93
Maximum115
Range111.5
Interquartile range (IQR)17

Descriptive statistics

Standard deviation14.09421032
Coefficient of variation (CV)0.2053850741
Kurtosis0.4427479699
Mean68.62334265
Median Absolute Deviation (MAD)9
Skewness0.1387964486
Sum270170.1
Variance198.6467644
MonotocityNot monotonic
2020-09-17T09:52:54.752563image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
601734.3%
 
701333.3%
 
711283.2%
 
661263.1%
 
681243.1%
 
611233.1%
 
651203.0%
 
641102.8%
 
721062.6%
 
631042.6%
 
Other values (86)269067.2%
 
ValueCountFrequency (%) 
3.51< 0.1%
 
7.61< 0.1%
 
141< 0.1%
 
171< 0.1%
 
181< 0.1%
 
ValueCountFrequency (%) 
11520.1%
 
11430.1%
 
1121< 0.1%
 
11120.1%
 
11060.1%
 

HR_max
Real number (ℝ≥0)

MISSING

Distinct141
Distinct (%)3.6%
Missing63
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean112.3975616
Minimum53
Maximum219
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:52:55.017935image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum53
5-th percentile81
Q196
median110
Q3125
95-th percentile152
Maximum219
Range166
Interquartile range (IQR)29

Descriptive statistics

Standard deviation22.15662544
Coefficient of variation (CV)0.1971272786
Kurtosis1.03454369
Mean112.3975616
Median Absolute Deviation (MAD)14
Skewness0.7158504757
Sum442509.2
Variance490.916051
MonotocityNot monotonic
2020-09-17T09:52:55.258518image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
99862.1%
 
95802.0%
 
104802.0%
 
102781.9%
 
114761.9%
 
120751.9%
 
118741.8%
 
105731.8%
 
91731.8%
 
96721.8%
 
Other values (131)317079.2%
 
ValueCountFrequency (%) 
531< 0.1%
 
5920.1%
 
601< 0.1%
 
621< 0.1%
 
6320.1%
 
ValueCountFrequency (%) 
2191< 0.1%
 
2171< 0.1%
 
2141< 0.1%
 
2111< 0.1%
 
2101< 0.1%
 

HR_med
Real number (ℝ≥0)

MISSING

Distinct170
Distinct (%)4.3%
Missing63
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean86.38354077
Minimum40
Maximum142
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:52:55.517100image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile62.9
Q176
median86
Q396
95-th percentile112
Maximum142
Range102
Interquartile range (IQR)20

Descriptive statistics

Standard deviation15.0081351
Coefficient of variation (CV)0.1737383646
Kurtosis0.004522556807
Mean86.38354077
Median Absolute Deviation (MAD)10
Skewness0.2690626208
Sum340092
Variance225.2441193
MonotocityNot monotonic
2020-09-17T09:52:55.758031image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
801563.9%
 
901122.8%
 
881122.8%
 
841052.6%
 
87902.2%
 
81892.2%
 
86872.2%
 
96812.0%
 
89812.0%
 
91802.0%
 
Other values (160)294473.6%
 
ValueCountFrequency (%) 
401< 0.1%
 
4420.1%
 
451< 0.1%
 
461< 0.1%
 
47.51< 0.1%
 
ValueCountFrequency (%) 
1421< 0.1%
 
1411< 0.1%
 
1391< 0.1%
 
1371< 0.1%
 
1361< 0.1%
 

NIDiasABP_min
Real number (ℝ≥0)

MISSING

Distinct85
Distinct (%)2.4%
Missing518
Missing (%)12.9%
Infinite0
Infinite (%)0.0%
Mean40.81447444
Minimum3
Maximum103
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:52:55.999695image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile21
Q132
median40
Q348
95-th percentile62.95
Maximum103
Range100
Interquartile range (IQR)16

Descriptive statistics

Standard deviation12.79163839
Coefficient of variation (CV)0.313409362
Kurtosis0.6375458875
Mean40.81447444
Median Absolute Deviation (MAD)8
Skewness0.4368486236
Sum142116
Variance163.6260128
MonotocityNot monotonic
2020-09-17T09:52:56.240657image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
391293.2%
 
421203.0%
 
451152.9%
 
371132.8%
 
431092.7%
 
401092.7%
 
441072.7%
 
311052.6%
 
381042.6%
 
411032.6%
 
Other values (75)236859.2%
 
(Missing)51813.0%
 
ValueCountFrequency (%) 
31< 0.1%
 
61< 0.1%
 
10100.2%
 
1150.1%
 
12100.2%
 
ValueCountFrequency (%) 
1031< 0.1%
 
971< 0.1%
 
921< 0.1%
 
911< 0.1%
 
9020.1%
 

NIDiasABP_max
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct130
Distinct (%)3.7%
Missing518
Missing (%)12.9%
Infinite0
Infinite (%)0.0%
Mean79.30499713
Minimum26
Maximum201
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:52:56.480959image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile50
Q166
median79
Q391
95-th percentile111
Maximum201
Range175
Interquartile range (IQR)25

Descriptive statistics

Standard deviation19.30765219
Coefficient of variation (CV)0.2434607262
Kurtosis1.76242607
Mean79.30499713
Median Absolute Deviation (MAD)12
Skewness0.6150164453
Sum276140
Variance372.7854332
MonotocityNot monotonic
2020-09-17T09:52:56.717337image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
80802.0%
 
74771.9%
 
89761.9%
 
75761.9%
 
73761.9%
 
88761.9%
 
87761.9%
 
77751.9%
 
85751.9%
 
67751.9%
 
Other values (120)272068.0%
 
(Missing)51813.0%
 
ValueCountFrequency (%) 
261< 0.1%
 
2950.1%
 
3020.1%
 
3120.1%
 
3220.1%
 
ValueCountFrequency (%) 
2011< 0.1%
 
18020.1%
 
1781< 0.1%
 
1741< 0.1%
 
1681< 0.1%
 

NIDiasABP_med
Real number (ℝ≥0)

MISSING

Distinct146
Distinct (%)4.2%
Missing518
Missing (%)12.9%
Infinite0
Infinite (%)0.0%
Mean56.78676048
Minimum22
Maximum110
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:52:56.974593image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile39
Q148.5
median56
Q364
95-th percentile78
Maximum110
Range88
Interquartile range (IQR)15.5

Descriptive statistics

Standard deviation12.09569855
Coefficient of variation (CV)0.213002088
Kurtosis0.4168749106
Mean56.78676048
Median Absolute Deviation (MAD)8
Skewness0.4669678379
Sum197731.5
Variance146.3059235
MonotocityNot monotonic
2020-09-17T09:52:57.195569image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
531152.9%
 
521072.7%
 
501032.6%
 
541022.5%
 
561012.5%
 
58952.4%
 
59902.2%
 
48892.2%
 
57852.1%
 
47852.1%
 
Other values (136)251062.7%
 
(Missing)51813.0%
 
ValueCountFrequency (%) 
2220.1%
 
23.51< 0.1%
 
251< 0.1%
 
25.51< 0.1%
 
261< 0.1%
 
ValueCountFrequency (%) 
1101< 0.1%
 
107.51< 0.1%
 
100.51< 0.1%
 
991< 0.1%
 
98.51< 0.1%
 

NIMAP_min
Real number (ℝ≥0)

MISSING

Distinct238
Distinct (%)6.8%
Missing520
Missing (%)13.0%
Infinite0
Infinite (%)0.0%
Mean59.69268103
Minimum7
Maximum121
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:52:57.568540image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile40.67
Q151
median58.67
Q367
95-th percentile82.347
Maximum121
Range114
Interquartile range (IQR)16

Descriptive statistics

Standard deviation13.03787374
Coefficient of variation (CV)0.2184166218
Kurtosis1.237364729
Mean59.69268103
Median Absolute Deviation (MAD)8
Skewness0.5723448905
Sum207730.53
Variance169.9861516
MonotocityNot monotonic
2020-09-17T09:52:57.806266image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
58751.9%
 
59741.8%
 
54711.8%
 
66641.6%
 
50631.6%
 
57631.6%
 
56591.5%
 
61591.5%
 
53591.5%
 
60581.5%
 
Other values (228)283570.9%
 
(Missing)52013.0%
 
ValueCountFrequency (%) 
71< 0.1%
 
181< 0.1%
 
191< 0.1%
 
2020.1%
 
2320.1%
 
ValueCountFrequency (%) 
1211< 0.1%
 
119.71< 0.1%
 
115.720.1%
 
114.71< 0.1%
 
111.71< 0.1%
 

NIMAP_max
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct317
Distinct (%)9.1%
Missing520
Missing (%)13.0%
Infinite0
Infinite (%)0.0%
Mean96.76192241
Minimum39.67
Maximum209
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:52:58.058555image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum39.67
5-th percentile68.67
Q184
median95.67
Q3107.7
95-th percentile128
Maximum209
Range169.33
Interquartile range (IQR)23.7

Descriptive statistics

Standard deviation18.91739825
Coefficient of variation (CV)0.1955045722
Kurtosis1.894313166
Mean96.76192241
Median Absolute Deviation (MAD)12
Skewness0.7093120136
Sum336731.49
Variance357.8679565
MonotocityNot monotonic
2020-09-17T09:52:58.286671image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
86511.3%
 
100441.1%
 
94441.1%
 
87441.1%
 
106431.1%
 
103431.1%
 
96431.1%
 
99421.1%
 
83421.1%
 
89421.1%
 
Other values (307)304276.0%
 
(Missing)52013.0%
 
ValueCountFrequency (%) 
39.671< 0.1%
 
421< 0.1%
 
43.331< 0.1%
 
471< 0.1%
 
47.331< 0.1%
 
ValueCountFrequency (%) 
2091< 0.1%
 
1991< 0.1%
 
196.71< 0.1%
 
1941< 0.1%
 
1891< 0.1%
 

NIMAP_med
Real number (ℝ≥0)

MISSING

Distinct521
Distinct (%)15.0%
Missing520
Missing (%)13.0%
Infinite0
Infinite (%)0.0%
Mean75.89954454
Minimum36.67
Maximum132
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:52:58.541515image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum36.67
5-th percentile58.33
Q167.33
median74.33
Q383
95-th percentile98.84325
Maximum132
Range95.33
Interquartile range (IQR)15.67

Descriptive statistics

Standard deviation12.44148958
Coefficient of variation (CV)0.1639204775
Kurtosis0.6767034096
Mean75.89954454
Median Absolute Deviation (MAD)7.67
Skewness0.6405990675
Sum264130.415
Variance154.790663
MonotocityNot monotonic
2020-09-17T09:52:58.777759image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
72671.7%
 
67601.5%
 
75591.5%
 
76531.3%
 
68521.3%
 
74511.3%
 
71501.2%
 
70451.1%
 
69441.1%
 
73431.1%
 
Other values (511)295673.9%
 
(Missing)52013.0%
 
ValueCountFrequency (%) 
36.671< 0.1%
 
39.671< 0.1%
 
421< 0.1%
 
42.51< 0.1%
 
461< 0.1%
 
ValueCountFrequency (%) 
1321< 0.1%
 
128.31< 0.1%
 
1261< 0.1%
 
12420.1%
 
1231< 0.1%
 

NISysABP_min
Real number (ℝ≥0)

MISSING

Distinct133
Distinct (%)3.8%
Missing515
Missing (%)12.9%
Infinite0
Infinite (%)0.0%
Mean94.30961263
Minimum1
Maximum234
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:52:59.014541image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile66
Q182
median92
Q3105
95-th percentile128
Maximum234
Range233
Interquartile range (IQR)23

Descriptive statistics

Standard deviation19.67559031
Coefficient of variation (CV)0.2086276231
Kurtosis2.668385537
Mean94.30961263
Median Absolute Deviation (MAD)11
Skewness0.5572754282
Sum328669
Variance387.1288542
MonotocityNot monotonic
2020-09-17T09:52:59.240395image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
911042.6%
 
901032.6%
 
95932.3%
 
92902.2%
 
88862.1%
 
87842.1%
 
93832.1%
 
85812.0%
 
89802.0%
 
94771.9%
 
Other values (123)260465.1%
 
(Missing)51512.9%
 
ValueCountFrequency (%) 
11< 0.1%
 
21< 0.1%
 
420.1%
 
1120.1%
 
121< 0.1%
 
ValueCountFrequency (%) 
2341< 0.1%
 
2111< 0.1%
 
18720.1%
 
1851< 0.1%
 
1811< 0.1%
 

NISysABP_max
Real number (ℝ≥0)

MISSING

Distinct162
Distinct (%)4.6%
Missing515
Missing (%)12.9%
Infinite0
Infinite (%)0.0%
Mean144.1911047
Minimum2
Maximum296
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:52:59.479989image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile105
Q1126
median142
Q3160
95-th percentile190
Maximum296
Range294
Interquartile range (IQR)34

Descriptive statistics

Standard deviation26.41232667
Coefficient of variation (CV)0.1831758396
Kurtosis1.093295539
Mean144.1911047
Median Absolute Deviation (MAD)17
Skewness0.5304378796
Sum502506
Variance697.6110001
MonotocityNot monotonic
2020-09-17T09:52:59.721051image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
142681.7%
 
133671.7%
 
138661.7%
 
137651.6%
 
128591.5%
 
131591.5%
 
135581.5%
 
149581.5%
 
147581.5%
 
134581.5%
 
Other values (152)286971.7%
 
(Missing)51512.9%
 
ValueCountFrequency (%) 
21< 0.1%
 
611< 0.1%
 
721< 0.1%
 
731< 0.1%
 
741< 0.1%
 
ValueCountFrequency (%) 
2961< 0.1%
 
2741< 0.1%
 
2601< 0.1%
 
2521< 0.1%
 
2511< 0.1%
 

NISysABP_med
Real number (ℝ≥0)

MISSING

Distinct209
Distinct (%)6.0%
Missing515
Missing (%)12.9%
Infinite0
Infinite (%)0.0%
Mean117.6355811
Minimum2
Maximum234
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:52:59.973442image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile92
Q1103.5
median115
Q3130
95-th percentile152
Maximum234
Range232
Interquartile range (IQR)26.5

Descriptive statistics

Standard deviation19.03685235
Coefficient of variation (CV)0.1618290332
Kurtosis1.158264938
Mean117.6355811
Median Absolute Deviation (MAD)13
Skewness0.6264062484
Sum409960
Variance362.4017474
MonotocityNot monotonic
2020-09-17T09:53:00.219001image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
112771.9%
 
102671.7%
 
111641.6%
 
108631.6%
 
114621.6%
 
109621.6%
 
113611.5%
 
104611.5%
 
107611.5%
 
115601.5%
 
Other values (199)284771.2%
 
(Missing)51512.9%
 
ValueCountFrequency (%) 
21< 0.1%
 
60.51< 0.1%
 
611< 0.1%
 
70.51< 0.1%
 
7230.1%
 
ValueCountFrequency (%) 
2341< 0.1%
 
2111< 0.1%
 
2051< 0.1%
 
2021< 0.1%
 
1951< 0.1%
 

RespRate_min
Real number (ℝ≥0)

MISSING

Distinct24
Distinct (%)2.2%
Missing2899
Missing (%)72.5%
Infinite0
Infinite (%)0.0%
Mean12.34514078
Minimum1
Maximum26
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:00.442590image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q110
median12
Q314
95-th percentile18
Maximum26
Range25
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.373270129
Coefficient of variation (CV)0.2732467932
Kurtosis0.2588399149
Mean12.34514078
Median Absolute Deviation (MAD)2
Skewness0.09022233515
Sum13592
Variance11.37895137
MonotocityNot monotonic
2020-09-17T09:53:00.652206image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%) 
121644.1%
 
111223.0%
 
141172.9%
 
131152.9%
 
10982.5%
 
16852.1%
 
15812.0%
 
9681.7%
 
8611.5%
 
7380.9%
 
Other values (14)1523.8%
 
(Missing)289972.5%
 
ValueCountFrequency (%) 
11< 0.1%
 
21< 0.1%
 
31< 0.1%
 
4100.2%
 
580.2%
 
ValueCountFrequency (%) 
261< 0.1%
 
2320.1%
 
2220.1%
 
2140.1%
 
20170.4%
 

RespRate_max
Real number (ℝ≥0)

MISSING

Distinct45
Distinct (%)4.1%
Missing2899
Missing (%)72.5%
Infinite0
Infinite (%)0.0%
Mean29.30063579
Minimum16
Maximum98
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:00.879853image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile21
Q125
median28
Q332
95-th percentile42
Maximum98
Range82
Interquartile range (IQR)7

Descriptive statistics

Standard deviation7.542093365
Coefficient of variation (CV)0.2574037444
Kurtosis17.84741002
Mean29.30063579
Median Absolute Deviation (MAD)4
Skewness2.837221519
Sum32260
Variance56.88317232
MonotocityNot monotonic
2020-09-17T09:53:01.287970image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%) 
28932.3%
 
26892.2%
 
27872.2%
 
24832.1%
 
25792.0%
 
30681.7%
 
23661.7%
 
29531.3%
 
32501.2%
 
22481.2%
 
Other values (35)3859.6%
 
(Missing)289972.5%
 
ValueCountFrequency (%) 
1620.1%
 
1750.1%
 
1850.1%
 
1980.2%
 
20260.7%
 
ValueCountFrequency (%) 
9820.1%
 
911< 0.1%
 
701< 0.1%
 
691< 0.1%
 
611< 0.1%
 

RespRate_med
Real number (ℝ≥0)

MISSING

Distinct46
Distinct (%)4.2%
Missing2899
Missing (%)72.5%
Infinite0
Infinite (%)0.0%
Mean19.44913715
Minimum9.5
Maximum40
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:01.590548image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum9.5
5-th percentile14
Q117
median19
Q322
95-th percentile26.5
Maximum40
Range30.5
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.033287817
Coefficient of variation (CV)0.2073761826
Kurtosis1.7081236
Mean19.44913715
Median Absolute Deviation (MAD)2.5
Skewness0.9108346791
Sum21413.5
Variance16.26741062
MonotocityNot monotonic
2020-09-17T09:53:01.822479image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%) 
191182.9%
 
181162.9%
 
171072.7%
 
201032.6%
 
161022.5%
 
22681.7%
 
21611.5%
 
23551.4%
 
15511.3%
 
14491.2%
 
Other values (36)2716.8%
 
(Missing)289972.5%
 
ValueCountFrequency (%) 
9.51< 0.1%
 
101< 0.1%
 
1140.1%
 
12130.3%
 
13150.4%
 
ValueCountFrequency (%) 
401< 0.1%
 
391< 0.1%
 
361< 0.1%
 
35.51< 0.1%
 
3420.1%
 

Temp_min
Real number (ℝ≥0)

MISSING

Distinct64
Distinct (%)1.6%
Missing64
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean35.89001524
Minimum26.7
Maximum38.8
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:02.067487image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum26.7
5-th percentile34.575
Q135.5
median36
Q336.4
95-th percentile37
Maximum38.8
Range12.1
Interquartile range (IQR)0.9

Descriptive statistics

Standard deviation0.8152232642
Coefficient of variation (CV)0.02271448643
Kurtosis8.718000108
Mean35.89001524
Median Absolute Deviation (MAD)0.4
Skewness-1.52597406
Sum141263.1
Variance0.6645889705
MonotocityNot monotonic
2020-09-17T09:53:02.317406image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
35.63137.8%
 
36.13077.7%
 
36.22506.2%
 
35.82175.4%
 
36.32165.4%
 
35.92135.3%
 
36.42025.1%
 
36.61934.8%
 
35.71854.6%
 
36.71674.2%
 
Other values (54)167341.8%
 
ValueCountFrequency (%) 
26.71< 0.1%
 
28.81< 0.1%
 
29.91< 0.1%
 
30.21< 0.1%
 
30.61< 0.1%
 
ValueCountFrequency (%) 
38.81< 0.1%
 
38.11< 0.1%
 
37.840.1%
 
37.7110.3%
 
37.690.2%
 

Temp_max
Real number (ℝ≥0)

MISSING

Distinct54
Distinct (%)1.4%
Missing64
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean37.92906504
Minimum35.6
Maximum42.1
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:02.572740image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum35.6
5-th percentile36.8
Q137.4
median37.8
Q338.4
95-th percentile39.3
Maximum42.1
Range6.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.7471683027
Coefficient of variation (CV)0.01969909624
Kurtosis0.6986618838
Mean37.92906504
Median Absolute Deviation (MAD)0.5
Skewness0.5475456618
Sum149288.8
Variance0.5582604725
MonotocityNot monotonic
2020-09-17T09:53:02.802790image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
37.82857.1%
 
37.72676.7%
 
37.62085.2%
 
37.42085.2%
 
38.32005.0%
 
37.91964.9%
 
37.21904.8%
 
38.11854.6%
 
38.21804.5%
 
37.31754.4%
 
Other values (44)184246.1%
 
ValueCountFrequency (%) 
35.630.1%
 
35.71< 0.1%
 
35.820.1%
 
36.160.1%
 
36.250.1%
 
ValueCountFrequency (%) 
42.11< 0.1%
 
41.220.1%
 
40.91< 0.1%
 
40.820.1%
 
40.740.1%
 

Temp_med
Real number (ℝ≥0)

MISSING

Distinct87
Distinct (%)2.2%
Missing64
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean37.05433181
Minimum33.4
Maximum39.8
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:03.049210image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum33.4
5-th percentile36.1
Q136.7
median37.05
Q337.4
95-th percentile38
Maximum39.8
Range6.4
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.5983604542
Coefficient of variation (CV)0.01614819172
Kurtosis1.182467109
Mean37.05433181
Median Absolute Deviation (MAD)0.35
Skewness-0.09708396906
Sum145845.85
Variance0.3580352332
MonotocityNot monotonic
2020-09-17T09:53:03.284205image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
37.22375.9%
 
36.72315.8%
 
37.32135.3%
 
36.82115.3%
 
36.92075.2%
 
37.12075.2%
 
371824.5%
 
37.41694.2%
 
37.71533.8%
 
36.61523.8%
 
Other values (77)197449.4%
 
ValueCountFrequency (%) 
33.41< 0.1%
 
33.71< 0.1%
 
341< 0.1%
 
34.21< 0.1%
 
34.2520.1%
 
ValueCountFrequency (%) 
39.820.1%
 
39.140.1%
 
391< 0.1%
 
38.940.1%
 
38.820.1%
 

Urine_min
Real number (ℝ≥0)

MISSING
ZEROS

Distinct81
Distinct (%)2.1%
Missing117
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean29.07167139
Minimum0
Maximum1100
Zeros664
Zeros (%)16.6%
Memory size31.2 KiB
2020-09-17T09:53:03.534479image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median20
Q330
95-th percentile100
Maximum1100
Range1100
Interquartile range (IQR)22

Descriptive statistics

Standard deviation50.53037289
Coefficient of variation (CV)1.738130987
Kurtosis83.52087579
Mean29.07167139
Median Absolute Deviation (MAD)10
Skewness6.955096022
Sum112885.3
Variance2553.318584
MonotocityNot monotonic
2020-09-17T09:53:03.760654image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
066416.6%
 
3040610.2%
 
203949.8%
 
153528.8%
 
103438.6%
 
252426.0%
 
51664.2%
 
401523.8%
 
501132.8%
 
35992.5%
 
Other values (71)95223.8%
 
(Missing)1172.9%
 
ValueCountFrequency (%) 
066416.6%
 
0.31< 0.1%
 
130.1%
 
2170.4%
 
390.2%
 
ValueCountFrequency (%) 
11001< 0.1%
 
60030.1%
 
5251< 0.1%
 
50020.1%
 
4701< 0.1%
 

Urine_max
Real number (ℝ≥0)

MISSING

Distinct415
Distinct (%)10.7%
Missing117
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean610.6469225
Minimum0
Maximum11000
Zeros7
Zeros (%)0.2%
Memory size31.2 KiB
2020-09-17T09:53:04.000547image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile100
Q1300
median450
Q3732
95-th percentile1618.5
Maximum11000
Range11000
Interquartile range (IQR)432

Descriptive statistics

Standard deviation581.3531146
Coefficient of variation (CV)0.9520282395
Kurtosis43.4893976
Mean610.6469225
Median Absolute Deviation (MAD)200
Skewness4.435175799
Sum2371142
Variance337971.4438
MonotocityNot monotonic
2020-09-17T09:53:04.237428image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
4002937.3%
 
5002185.5%
 
6001824.5%
 
3001764.4%
 
4501644.1%
 
2001193.0%
 
7001102.8%
 
800992.5%
 
1000892.2%
 
350721.8%
 
Other values (405)236159.0%
 
(Missing)1172.9%
 
ValueCountFrequency (%) 
070.2%
 
320.1%
 
520.1%
 
61< 0.1%
 
820.1%
 
ValueCountFrequency (%) 
110001< 0.1%
 
80001< 0.1%
 
64001< 0.1%
 
52001< 0.1%
 
50001< 0.1%
 

Urine_med
Real number (ℝ≥0)

MISSING
ZEROS

Distinct213
Distinct (%)5.5%
Missing117
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean102.5900077
Minimum0
Maximum3112.5
Zeros46
Zeros (%)1.1%
Memory size31.2 KiB
2020-09-17T09:53:04.481005image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile20
Q145
median80
Q3120
95-th percentile280
Maximum3112.5
Range3112.5
Interquartile range (IQR)75

Descriptive statistics

Standard deviation118.0884579
Coefficient of variation (CV)1.151071733
Kurtosis223.3716732
Mean102.5900077
Median Absolute Deviation (MAD)35
Skewness10.61678165
Sum398357
Variance13944.8839
MonotocityNot monotonic
2020-09-17T09:53:04.879270image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1003488.7%
 
603308.2%
 
803037.6%
 
502155.4%
 
402015.0%
 
451403.5%
 
1201393.5%
 
301263.1%
 
140962.4%
 
70912.3%
 
Other values (203)189447.3%
 
(Missing)1172.9%
 
ValueCountFrequency (%) 
0461.1%
 
11< 0.1%
 
2.51< 0.1%
 
320.1%
 
3.51< 0.1%
 
ValueCountFrequency (%) 
3112.51< 0.1%
 
30001< 0.1%
 
20001< 0.1%
 
100020.1%
 
8501< 0.1%
 

HCT_min
Real number (ℝ≥0)

MISSING

Distinct299
Distinct (%)7.6%
Missing64
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean28.63445122
Minimum9
Maximum60.3
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:05.143273image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile20.975
Q124.9
median28
Q332
95-th percentile38.1
Maximum60.3
Range51.3
Interquartile range (IQR)7.1

Descriptive statistics

Standard deviation5.344967076
Coefficient of variation (CV)0.1866621097
Kurtosis0.4514545675
Mean28.63445122
Median Absolute Deviation (MAD)3.4
Skewness0.4176630221
Sum112705.2
Variance28.56867304
MonotocityNot monotonic
2020-09-17T09:53:05.391180image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
27.1421.1%
 
24.7411.0%
 
26.9391.0%
 
27.7391.0%
 
29.6380.9%
 
26.6370.9%
 
28.1370.9%
 
27.2370.9%
 
26.3360.9%
 
27.9360.9%
 
Other values (289)355488.8%
 
(Missing)641.6%
 
ValueCountFrequency (%) 
91< 0.1%
 
10.31< 0.1%
 
11.11< 0.1%
 
11.81< 0.1%
 
12.51< 0.1%
 
ValueCountFrequency (%) 
60.31< 0.1%
 
50.61< 0.1%
 
49.61< 0.1%
 
47.91< 0.1%
 
47.21< 0.1%
 

HCT_max
Real number (ℝ≥0)

MISSING

Distinct273
Distinct (%)6.9%
Missing64
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean34.14247967
Minimum18.6
Maximum61.8
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:05.641157image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum18.6
5-th percentile27.2
Q130.8
median33.5
Q337.025
95-th percentile42.825
Maximum61.8
Range43.2
Interquartile range (IQR)6.225

Descriptive statistics

Standard deviation4.793684362
Coefficient of variation (CV)0.1404023494
Kurtosis1.021396511
Mean34.14247967
Median Absolute Deviation (MAD)3
Skewness0.6887612542
Sum134384.8
Variance22.97940976
MonotocityNot monotonic
2020-09-17T09:53:05.869609image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
32.5501.2%
 
31.6461.1%
 
30.5431.1%
 
33.5431.1%
 
32431.1%
 
33.9421.1%
 
31.3411.0%
 
31.7411.0%
 
32.3401.0%
 
29.8391.0%
 
Other values (263)350887.7%
 
(Missing)641.6%
 
ValueCountFrequency (%) 
18.61< 0.1%
 
21.81< 0.1%
 
22.720.1%
 
22.930.1%
 
231< 0.1%
 
ValueCountFrequency (%) 
61.81< 0.1%
 
61.21< 0.1%
 
54.51< 0.1%
 
54.41< 0.1%
 
53.31< 0.1%
 

HCT_med
Real number (ℝ≥0)

MISSING

Distinct518
Distinct (%)13.2%
Missing64
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean31.45693598
Minimum14.8
Maximum61.05
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:06.122605image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum14.8
5-th percentile24.95
Q128.3
median30.775
Q334.1
95-th percentile39.9
Maximum61.05
Range46.25
Interquartile range (IQR)5.8

Descriptive statistics

Standard deviation4.562617164
Coefficient of variation (CV)0.1450432797
Kurtosis0.9086941072
Mean31.45693598
Median Absolute Deviation (MAD)2.825
Skewness0.716130457
Sum123814.5
Variance20.81747539
MonotocityNot monotonic
2020-09-17T09:53:06.369864image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
29.6380.9%
 
30350.9%
 
30.9350.9%
 
30.4340.9%
 
28.9340.9%
 
31.1330.8%
 
30.3330.8%
 
28.6330.8%
 
27.5320.8%
 
29320.8%
 
Other values (508)359789.9%
 
(Missing)641.6%
 
ValueCountFrequency (%) 
14.81< 0.1%
 
16.31< 0.1%
 
20.51< 0.1%
 
21.31< 0.1%
 
21.520.1%
 
ValueCountFrequency (%) 
61.051< 0.1%
 
52.351< 0.1%
 
50.61< 0.1%
 
49.551< 0.1%
 
49.51< 0.1%
 

BUN_min
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct121
Distinct (%)3.1%
Missing64
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean21.92555894
Minimum0
Maximum165
Zeros1
Zeros (%)< 0.1%
Memory size31.2 KiB
2020-09-17T09:53:06.613113image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q111
median16
Q326
95-th percentile61
Maximum165
Range165
Interquartile range (IQR)15

Descriptive statistics

Standard deviation18.67367865
Coefficient of variation (CV)0.8516854094
Kurtosis8.548178676
Mean21.92555894
Median Absolute Deviation (MAD)7
Skewness2.531570266
Sum86299
Variance348.7062741
MonotocityNot monotonic
2020-09-17T09:53:06.848545image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
102095.2%
 
141985.0%
 
121985.0%
 
111944.9%
 
91874.7%
 
131764.4%
 
81624.0%
 
161563.9%
 
171553.9%
 
151503.8%
 
Other values (111)215153.8%
 
ValueCountFrequency (%) 
01< 0.1%
 
2160.4%
 
3380.9%
 
4441.1%
 
5721.8%
 
ValueCountFrequency (%) 
1651< 0.1%
 
1571< 0.1%
 
1481< 0.1%
 
1451< 0.1%
 
1351< 0.1%
 

BUN_max
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct141
Distinct (%)3.6%
Missing64
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean29.31224593
Minimum3
Maximum197
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:07.117518image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile9
Q115
median22
Q335.25
95-th percentile77
Maximum197
Range194
Interquartile range (IQR)20.25

Descriptive statistics

Standard deviation23.21652814
Coefficient of variation (CV)0.7920419403
Kurtosis7.65178056
Mean29.31224593
Median Absolute Deviation (MAD)9
Skewness2.381957101
Sum115373
Variance539.0071791
MonotocityNot monotonic
2020-09-17T09:53:07.361858image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
141654.1%
 
161624.0%
 
121513.8%
 
151483.7%
 
171483.7%
 
201423.5%
 
131413.5%
 
191413.5%
 
181283.2%
 
231223.0%
 
Other values (131)248862.2%
 
ValueCountFrequency (%) 
330.1%
 
4130.3%
 
5210.5%
 
6270.7%
 
7521.3%
 
ValueCountFrequency (%) 
1971< 0.1%
 
1861< 0.1%
 
1841< 0.1%
 
1781< 0.1%
 
1751< 0.1%
 

BUN_med
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct215
Distinct (%)5.5%
Missing64
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean25.48043699
Minimum2
Maximum176.5
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:07.629280image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile7
Q113
median18.5
Q330
95-th percentile69.125
Maximum176.5
Range174.5
Interquartile range (IQR)17

Descriptive statistics

Standard deviation20.9073043
Coefficient of variation (CV)0.8205237732
Kurtosis7.93763214
Mean25.48043699
Median Absolute Deviation (MAD)7.5
Skewness2.437554573
Sum100291
Variance437.1153732
MonotocityNot monotonic
2020-09-17T09:53:07.861704image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
131473.7%
 
111333.3%
 
141323.3%
 
151293.2%
 
121263.1%
 
181233.1%
 
161203.0%
 
101193.0%
 
171052.6%
 
81012.5%
 
Other values (205)270167.5%
 
ValueCountFrequency (%) 
21< 0.1%
 
2.51< 0.1%
 
3140.4%
 
3.540.1%
 
4220.5%
 
ValueCountFrequency (%) 
176.51< 0.1%
 
1721< 0.1%
 
168.51< 0.1%
 
16420.1%
 
1541< 0.1%
 

Creatinine_min
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct95
Distinct (%)2.4%
Missing64
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean1.183714431
Minimum0.1
Maximum13.3
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:08.111631image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.4
Q10.6
median0.8
Q31.2
95-th percentile3.3
Maximum13.3
Range13.2
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation1.208404084
Coefficient of variation (CV)1.020857778
Kurtosis22.11553877
Mean1.183714431
Median Absolute Deviation (MAD)0.2
Skewness4.105429061
Sum4659.1
Variance1.460240431
MonotocityNot monotonic
2020-09-17T09:53:08.520802image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.752213.1%
 
0.649512.4%
 
0.844011.0%
 
0.93578.9%
 
0.53468.6%
 
12807.0%
 
0.41864.7%
 
1.11794.5%
 
1.21573.9%
 
1.31162.9%
 
Other values (85)85821.4%
 
ValueCountFrequency (%) 
0.120.1%
 
0.2150.4%
 
0.3661.7%
 
0.41864.7%
 
0.53468.6%
 
ValueCountFrequency (%) 
13.31< 0.1%
 
12.61< 0.1%
 
12.41< 0.1%
 
11.61< 0.1%
 
11.21< 0.1%
 

Creatinine_max
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct114
Distinct (%)2.9%
Missing64
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean1.560137195
Minimum0.2
Maximum22.1
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:08.779596image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.5
Q10.8
median1
Q31.6
95-th percentile4.525
Maximum22.1
Range21.9
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation1.65314344
Coefficient of variation (CV)1.059614145
Kurtosis25.24006937
Mean1.560137195
Median Absolute Deviation (MAD)0.3
Skewness4.172025648
Sum6140.7
Variance2.732883234
MonotocityNot monotonic
2020-09-17T09:53:09.022366image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.944011.0%
 
0.73759.4%
 
0.83729.3%
 
13468.6%
 
1.12837.1%
 
0.62676.7%
 
1.22035.1%
 
1.31694.2%
 
1.41493.7%
 
0.51463.6%
 
Other values (104)118629.6%
 
ValueCountFrequency (%) 
0.21< 0.1%
 
0.3130.3%
 
0.4571.4%
 
0.51463.6%
 
0.62676.7%
 
ValueCountFrequency (%) 
22.11< 0.1%
 
221< 0.1%
 
14.81< 0.1%
 
13.91< 0.1%
 
13.61< 0.1%
 

Creatinine_med
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct191
Distinct (%)4.9%
Missing64
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean1.36878811
Minimum0.2
Maximum16.1
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:09.266872image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.5
Q10.7
median0.9
Q31.4
95-th percentile3.8
Maximum16.1
Range15.9
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation1.425543265
Coefficient of variation (CV)1.041463799
Kurtosis21.07640854
Mean1.36878811
Median Absolute Deviation (MAD)0.3
Skewness4.029980042
Sum5387.55
Variance2.032173601
MonotocityNot monotonic
2020-09-17T09:53:09.498925image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.73649.1%
 
0.83398.5%
 
0.93378.4%
 
0.62927.3%
 
12516.3%
 
0.51914.8%
 
1.11834.6%
 
1.21473.7%
 
0.75992.5%
 
0.65942.4%
 
Other values (181)163941.0%
 
ValueCountFrequency (%) 
0.250.1%
 
0.2530.1%
 
0.3210.5%
 
0.3580.2%
 
0.4771.9%
 
ValueCountFrequency (%) 
16.11< 0.1%
 
14.11< 0.1%
 
13.21< 0.1%
 
13.151< 0.1%
 
12.81< 0.1%
 

Glucose_min
Real number (ℝ≥0)

MISSING

Distinct199
Distinct (%)5.1%
Missing113
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean108.6333934
Minimum10
Maximum298
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:09.760573image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile65
Q190
median106
Q3124
95-th percentile160
Maximum298
Range288
Interquartile range (IQR)34

Descriptive statistics

Standard deviation30.01296077
Coefficient of variation (CV)0.2762774856
Kurtosis3.192754401
Mean108.6333934
Median Absolute Deviation (MAD)17
Skewness0.9673391587
Sum422258
Variance900.7778141
MonotocityNot monotonic
2020-09-17T09:53:09.991904image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
105741.8%
 
99711.8%
 
95701.8%
 
89691.7%
 
107671.7%
 
106661.7%
 
118651.6%
 
108641.6%
 
119641.6%
 
112631.6%
 
Other values (189)321480.3%
 
(Missing)1132.8%
 
ValueCountFrequency (%) 
101< 0.1%
 
231< 0.1%
 
241< 0.1%
 
2920.1%
 
3020.1%
 
ValueCountFrequency (%) 
2981< 0.1%
 
2871< 0.1%
 
27920.1%
 
2701< 0.1%
 
2641< 0.1%
 

Glucose_max
Real number (ℝ≥0)

MISSING

Distinct385
Distinct (%)9.9%
Missing113
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean173.592745
Minimum60
Maximum1143
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:10.251260image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum60
5-th percentile98.3
Q1126
median151
Q3192
95-th percentile316.7
Maximum1143
Range1083
Interquartile range (IQR)66

Descriptive statistics

Standard deviation87.96284879
Coefficient of variation (CV)0.5067196142
Kurtosis26.09458806
Mean173.592745
Median Absolute Deviation (MAD)30
Skewness4.00754263
Sum674755
Variance7737.462768
MonotocityNot monotonic
2020-09-17T09:53:10.476468image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
146521.3%
 
124501.2%
 
138491.2%
 
147451.1%
 
140451.1%
 
130441.1%
 
127441.1%
 
129431.1%
 
151421.1%
 
134421.1%
 
Other values (375)343185.8%
 
(Missing)1132.8%
 
ValueCountFrequency (%) 
601< 0.1%
 
611< 0.1%
 
621< 0.1%
 
651< 0.1%
 
6630.1%
 
ValueCountFrequency (%) 
11431< 0.1%
 
10971< 0.1%
 
10951< 0.1%
 
10751< 0.1%
 
9151< 0.1%
 

Glucose_med
Real number (ℝ≥0)

MISSING

Distinct387
Distinct (%)10.0%
Missing113
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean133.6385387
Minimum49
Maximum531
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:10.704491image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum49
5-th percentile88
Q1109
median127
Q3149
95-th percentile205.85
Maximum531
Range482
Interquartile range (IQR)40

Descriptive statistics

Standard deviation38.540758
Coefficient of variation (CV)0.288395536
Kurtosis7.675806335
Mean133.6385387
Median Absolute Deviation (MAD)19.5
Skewness1.863165096
Sum519453
Variance1485.390028
MonotocityNot monotonic
2020-09-17T09:53:10.937164image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
116551.4%
 
127541.4%
 
112511.3%
 
115471.2%
 
129471.2%
 
135461.1%
 
130461.1%
 
113461.1%
 
118451.1%
 
122441.1%
 
Other values (377)340685.2%
 
(Missing)1132.8%
 
ValueCountFrequency (%) 
491< 0.1%
 
52.51< 0.1%
 
56.51< 0.1%
 
581< 0.1%
 
5920.1%
 
ValueCountFrequency (%) 
5311< 0.1%
 
4301< 0.1%
 
3911< 0.1%
 
382.51< 0.1%
 
368.51< 0.1%
 

HCO3_min
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct40
Distinct (%)1.0%
Missing76
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean21.84852192
Minimum5
Maximum44
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:11.174662image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile14
Q119
median22
Q324
95-th percentile29
Maximum44
Range39
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.522719192
Coefficient of variation (CV)0.2070034399
Kurtosis1.58398694
Mean21.84852192
Median Absolute Deviation (MAD)3
Skewness-0.0613520645
Sum85733.6
Variance20.45498889
MonotocityNot monotonic
2020-09-17T09:53:11.387702image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%) 
2243310.8%
 
2342210.5%
 
2440010.0%
 
213388.5%
 
203198.0%
 
253017.5%
 
192706.8%
 
262355.9%
 
181934.8%
 
171503.8%
 
Other values (30)86321.6%
 
ValueCountFrequency (%) 
560.1%
 
61< 0.1%
 
760.1%
 
8140.4%
 
9120.3%
 
ValueCountFrequency (%) 
4420.1%
 
421< 0.1%
 
411< 0.1%
 
4020.1%
 
3940.1%
 

HCO3_max
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct37
Distinct (%)0.9%
Missing76
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean25.4283894
Minimum11
Maximum50
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:11.629705image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile19
Q123
median25
Q328
95-th percentile32
Maximum50
Range39
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.074781385
Coefficient of variation (CV)0.1602453589
Kurtosis2.18357561
Mean25.4283894
Median Absolute Deviation (MAD)2
Skewness0.5218673276
Sum99781
Variance16.60384334
MonotocityNot monotonic
2020-09-17T09:53:11.845694image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%) 
2549412.3%
 
2646011.5%
 
2739910.0%
 
243729.3%
 
233679.2%
 
283117.8%
 
222506.2%
 
292345.9%
 
211533.8%
 
201523.8%
 
Other values (27)73218.3%
 
ValueCountFrequency (%) 
111< 0.1%
 
1220.1%
 
1340.1%
 
1470.2%
 
15140.4%
 
ValueCountFrequency (%) 
501< 0.1%
 
471< 0.1%
 
4540.1%
 
4440.1%
 
431< 0.1%
 

HCO3_med
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct66
Distinct (%)1.7%
Missing76
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean23.66156983
Minimum8
Maximum47
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:12.238679image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile17
Q121
median24
Q326
95-th percentile30.5
Maximum47
Range39
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.126429328
Coefficient of variation (CV)0.1743937261
Kurtosis2.050297521
Mean23.66156983
Median Absolute Deviation (MAD)2
Skewness0.3402036995
Sum92848
Variance17.027419
MonotocityNot monotonic
2020-09-17T09:53:12.472215image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
253498.7%
 
243428.6%
 
233127.8%
 
222987.4%
 
262817.0%
 
212265.7%
 
271854.6%
 
201714.3%
 
24.51363.4%
 
191323.3%
 
Other values (56)149237.3%
 
ValueCountFrequency (%) 
81< 0.1%
 
91< 0.1%
 
9.51< 0.1%
 
1020.1%
 
10.51< 0.1%
 
ValueCountFrequency (%) 
4720.1%
 
4420.1%
 
4330.1%
 
40.51< 0.1%
 
39.51< 0.1%
 

Mg_min
Real number (ℝ≥0)

MISSING

Distinct29
Distinct (%)0.7%
Missing103
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean1.788401334
Minimum0.6
Maximum4.3
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:12.703437image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.6
5-th percentile1.3
Q11.6
median1.8
Q32
95-th percentile2.4
Maximum4.3
Range3.7
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.3413629751
Coefficient of variation (CV)0.1908760459
Kurtosis1.792753905
Mean1.788401334
Median Absolute Deviation (MAD)0.2
Skewness0.5084598694
Sum6969.4
Variance0.1165286808
MonotocityNot monotonic
2020-09-17T09:53:12.912667image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%) 
1.756014.0%
 
1.853513.4%
 
1.946811.7%
 
1.641810.4%
 
23338.3%
 
1.53198.0%
 
2.12426.0%
 
1.41884.7%
 
2.21734.3%
 
1.31553.9%
 
Other values (19)50612.7%
 
ValueCountFrequency (%) 
0.620.1%
 
0.850.1%
 
0.990.2%
 
1300.8%
 
1.1681.7%
 
ValueCountFrequency (%) 
4.31< 0.1%
 
3.41< 0.1%
 
3.320.1%
 
3.220.1%
 
3.130.1%
 

Mg_max
Real number (ℝ≥0)

MISSING

Distinct39
Distinct (%)1.0%
Missing103
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean2.256864255
Minimum1.1
Maximum9.9
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:13.157763image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile1.7
Q12
median2.2
Q32.4
95-th percentile3
Maximum9.9
Range8.8
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.4270195648
Coefficient of variation (CV)0.1892092375
Kurtosis38.85794792
Mean2.256864255
Median Absolute Deviation (MAD)0.2
Skewness3.356343646
Sum8795
Variance0.1823457087
MonotocityNot monotonic
2020-09-17T09:53:13.368498image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%) 
2.153813.5%
 
2.249912.5%
 
246011.5%
 
2.340510.1%
 
2.43308.2%
 
1.93278.2%
 
2.52446.1%
 
1.81864.7%
 
2.61844.6%
 
2.71323.3%
 
Other values (29)59214.8%
 
ValueCountFrequency (%) 
1.11< 0.1%
 
1.230.1%
 
1.350.1%
 
1.4100.2%
 
1.5250.6%
 
ValueCountFrequency (%) 
9.91< 0.1%
 
7.71< 0.1%
 
6.31< 0.1%
 
61< 0.1%
 
5.51< 0.1%
 

Mg_med
Real number (ℝ≥0)

MISSING

Distinct60
Distinct (%)1.5%
Missing103
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean2.014870413
Minimum1.1
Maximum6.5
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:13.592980image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile1.6
Q11.8
median2
Q32.2
95-th percentile2.55
Maximum6.5
Range5.4
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.3108021679
Coefficient of variation (CV)0.1542541723
Kurtosis14.54281483
Mean2.014870413
Median Absolute Deviation (MAD)0.2
Skewness1.775382778
Sum7851.95
Variance0.09659798757
MonotocityNot monotonic
2020-09-17T09:53:13.847871image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.949712.4%
 
244411.1%
 
2.13809.5%
 
1.83177.9%
 
2.22666.7%
 
1.72205.5%
 
2.31944.9%
 
1.851493.7%
 
1.951493.7%
 
2.051403.5%
 
Other values (50)114128.5%
 
ValueCountFrequency (%) 
1.11< 0.1%
 
1.151< 0.1%
 
1.250.1%
 
1.2540.1%
 
1.390.2%
 
ValueCountFrequency (%) 
6.51< 0.1%
 
4.751< 0.1%
 
41< 0.1%
 
3.720.1%
 
3.61< 0.1%
 

Platelets_min
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct479
Distinct (%)12.2%
Missing68
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean179.4262462
Minimum6
Maximum918
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:14.087629image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile55
Q1114
median162
Q3224
95-th percentile363
Maximum918
Range912
Interquartile range (IQR)110

Descriptive statistics

Standard deviation99.22173059
Coefficient of variation (CV)0.5529945184
Kurtosis4.636495077
Mean179.4262462
Median Absolute Deviation (MAD)53
Skewness1.55595553
Sum705504
Variance9844.951822
MonotocityNot monotonic
2020-09-17T09:53:14.323101image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
162310.8%
 
135300.8%
 
139300.8%
 
144270.7%
 
159260.7%
 
134260.7%
 
132260.7%
 
130260.7%
 
152250.6%
 
145250.6%
 
Other values (469)366091.5%
 
(Missing)681.7%
 
ValueCountFrequency (%) 
61< 0.1%
 
71< 0.1%
 
81< 0.1%
 
91< 0.1%
 
101< 0.1%
 
ValueCountFrequency (%) 
9181< 0.1%
 
8981< 0.1%
 
7861< 0.1%
 
7251< 0.1%
 
6991< 0.1%
 

Platelets_max
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct538
Distinct (%)13.7%
Missing68
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean233.9984741
Minimum16
Maximum1047
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:14.573926image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile96
Q1160
median213
Q3280
95-th percentile440.45
Maximum1047
Range1031
Interquartile range (IQR)120

Descriptive statistics

Standard deviation113.4039253
Coefficient of variation (CV)0.4846353196
Kurtosis5.468264273
Mean233.9984741
Median Absolute Deviation (MAD)59
Skewness1.679258995
Sum920082
Variance12860.45026
MonotocityNot monotonic
2020-09-17T09:53:14.831750image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
203310.8%
 
197290.7%
 
200270.7%
 
188260.7%
 
222250.6%
 
202250.6%
 
226240.6%
 
225230.6%
 
175230.6%
 
171230.6%
 
Other values (528)367691.9%
 
(Missing)681.7%
 
ValueCountFrequency (%) 
161< 0.1%
 
181< 0.1%
 
231< 0.1%
 
241< 0.1%
 
271< 0.1%
 
ValueCountFrequency (%) 
10471< 0.1%
 
10191< 0.1%
 
9921< 0.1%
 
9691< 0.1%
 
9401< 0.1%
 

Platelets_med
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct829
Distinct (%)21.1%
Missing68
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean204.0422177
Minimum11.5
Maximum919
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:15.085896image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum11.5
5-th percentile75.275
Q1134.5
median186
Q3249
95-th percentile393.5
Maximum919
Range907.5
Interquartile range (IQR)114.5

Descriptive statistics

Standard deviation103.598853
Coefficient of variation (CV)0.5077324395
Kurtosis4.540926289
Mean204.0422177
Median Absolute Deviation (MAD)56
Skewness1.548331961
Sum802294
Variance10732.72233
MonotocityNot monotonic
2020-09-17T09:53:15.321756image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
185260.7%
 
118220.5%
 
188200.5%
 
160200.5%
 
184200.5%
 
221190.5%
 
150190.5%
 
227190.5%
 
194190.5%
 
134190.5%
 
Other values (819)372993.2%
 
(Missing)681.7%
 
ValueCountFrequency (%) 
11.51< 0.1%
 
14.51< 0.1%
 
151< 0.1%
 
161< 0.1%
 
171< 0.1%
 
ValueCountFrequency (%) 
9191< 0.1%
 
8981< 0.1%
 
8911< 0.1%
 
8371< 0.1%
 
8211< 0.1%
 

K_min
Real number (ℝ≥0)

MISSING

Distinct43
Distinct (%)1.1%
Missing96
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean3.759503074
Minimum1.8
Maximum7.6
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:15.554186image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1.8
5-th percentile3
Q13.4
median3.7
Q34.1
95-th percentile4.6
Maximum7.6
Range5.8
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.5165881279
Coefficient of variation (CV)0.1374086196
Kurtosis1.361814888
Mean3.759503074
Median Absolute Deviation (MAD)0.3
Skewness0.3690003538
Sum14677.1
Variance0.2668632939
MonotocityNot monotonic
2020-09-17T09:53:15.909789image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%) 
3.73428.6%
 
3.63298.2%
 
3.83127.8%
 
3.53097.7%
 
3.92646.6%
 
3.42526.3%
 
42506.2%
 
3.32325.8%
 
4.12135.3%
 
4.21884.7%
 
Other values (33)121330.3%
 
ValueCountFrequency (%) 
1.81< 0.1%
 
1.91< 0.1%
 
21< 0.1%
 
2.140.1%
 
2.240.1%
 
ValueCountFrequency (%) 
7.61< 0.1%
 
6.71< 0.1%
 
6.31< 0.1%
 
5.81< 0.1%
 
5.61< 0.1%
 

K_max
Real number (ℝ≥0)

MISSING

Distinct69
Distinct (%)1.8%
Missing96
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean4.558990779
Minimum2.5
Maximum22.9
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:16.162918image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile3.7
Q14.1
median4.4
Q34.8
95-th percentile5.9
Maximum22.9
Range20.4
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation0.8258047455
Coefficient of variation (CV)0.1811376214
Kurtosis77.90702649
Mean4.558990779
Median Absolute Deviation (MAD)0.4
Skewness5.229539676
Sum17798.3
Variance0.6819534778
MonotocityNot monotonic
2020-09-17T09:53:16.400877image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
4.23288.2%
 
4.33258.1%
 
4.62887.2%
 
4.42787.0%
 
4.12756.9%
 
4.52586.5%
 
42486.2%
 
3.92055.1%
 
4.71924.8%
 
4.81674.2%
 
Other values (59)134033.5%
 
ValueCountFrequency (%) 
2.51< 0.1%
 
31< 0.1%
 
3.140.1%
 
3.240.1%
 
3.3130.3%
 
ValueCountFrequency (%) 
22.91< 0.1%
 
15.61< 0.1%
 
12.71< 0.1%
 
10.91< 0.1%
 
101< 0.1%
 

K_med
Real number (ℝ≥0)

MISSING

Distinct87
Distinct (%)2.2%
Missing96
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean4.114241803
Minimum2.5
Maximum7.6
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:16.665022image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile3.4
Q13.8
median4.1
Q34.4
95-th percentile5
Maximum7.6
Range5.1
Interquartile range (IQR)0.6

Descriptive statistics

Standard deviation0.4860804337
Coefficient of variation (CV)0.1181458108
Kurtosis1.748183451
Mean4.114241803
Median Absolute Deviation (MAD)0.3
Skewness0.7624601671
Sum16062
Variance0.236274188
MonotocityNot monotonic
2020-09-17T09:53:16.910944image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3.92987.4%
 
4.12646.6%
 
3.82526.3%
 
42426.0%
 
3.72205.5%
 
4.22075.2%
 
4.31684.2%
 
4.41594.0%
 
3.61523.8%
 
4.51473.7%
 
Other values (77)179544.9%
 
ValueCountFrequency (%) 
2.51< 0.1%
 
2.71< 0.1%
 
2.820.1%
 
2.940.1%
 
2.91< 0.1%
 
ValueCountFrequency (%) 
7.61< 0.1%
 
6.71< 0.1%
 
6.420.1%
 
6.31< 0.1%
 
6.21< 0.1%
 

Na_min
Real number (ℝ≥0)

MISSING

Distinct45
Distinct (%)1.1%
Missing75
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean136.9225478
Minimum98
Maximum155
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:17.162464image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum98
5-th percentile130
Q1135
median137
Q3140
95-th percentile143
Maximum155
Range57
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.409714236
Coefficient of variation (CV)0.03220590259
Kurtosis6.186876855
Mean136.9225478
Median Absolute Deviation (MAD)2
Skewness-1.032716726
Sum537421
Variance19.44557964
MonotocityNot monotonic
2020-09-17T09:53:17.386539image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%) 
13744611.2%
 
13842110.5%
 
1393909.8%
 
1363739.3%
 
1353488.7%
 
1403448.6%
 
1342516.3%
 
1412245.6%
 
1331914.8%
 
1421584.0%
 
Other values (35)77919.5%
 
ValueCountFrequency (%) 
981< 0.1%
 
1001< 0.1%
 
10320.1%
 
1081< 0.1%
 
1141< 0.1%
 
ValueCountFrequency (%) 
1551< 0.1%
 
1541< 0.1%
 
15320.1%
 
1521< 0.1%
 
15060.1%
 

Na_max
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct46
Distinct (%)1.2%
Missing75
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean140.7207643
Minimum119
Maximum177
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:17.632618image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum119
5-th percentile134
Q1138
median141
Q3143
95-th percentile148
Maximum177
Range58
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.428442202
Coefficient of variation (CV)0.03146971396
Kurtosis5.348169837
Mean140.7207643
Median Absolute Deviation (MAD)2
Skewness0.8953468328
Sum552329
Variance19.61110033
MonotocityNot monotonic
2020-09-17T09:53:17.853476image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%) 
14046011.5%
 
14144811.2%
 
1393909.8%
 
1423689.2%
 
1383198.0%
 
1433198.0%
 
1372436.1%
 
1442305.8%
 
1361844.6%
 
1451784.5%
 
Other values (36)78619.7%
 
ValueCountFrequency (%) 
1191< 0.1%
 
1231< 0.1%
 
1241< 0.1%
 
12520.1%
 
12620.1%
 
ValueCountFrequency (%) 
17720.1%
 
1711< 0.1%
 
1701< 0.1%
 
1661< 0.1%
 
16520.1%
 

Na_med
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct75
Distinct (%)1.9%
Missing75
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean138.8978344
Minimum111.5
Maximum167
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:18.098986image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum111.5
5-th percentile132.5
Q1136.5
median139
Q3141
95-th percentile145.5
Maximum167
Range55.5
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation4.144667662
Coefficient of variation (CV)0.02983968526
Kurtosis3.435816335
Mean138.8978344
Median Absolute Deviation (MAD)2.5
Skewness0.1405106923
Sum545174
Variance17.17827003
MonotocityNot monotonic
2020-09-17T09:53:18.325088image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1393418.5%
 
1403348.3%
 
1382947.3%
 
1372746.9%
 
1412546.3%
 
1362385.9%
 
1422225.5%
 
1431503.8%
 
1351493.7%
 
139.51333.3%
 
Other values (65)153638.4%
 
ValueCountFrequency (%) 
111.51< 0.1%
 
1151< 0.1%
 
1201< 0.1%
 
1211< 0.1%
 
12320.1%
 
ValueCountFrequency (%) 
1671< 0.1%
 
1651< 0.1%
 
1641< 0.1%
 
1611< 0.1%
 
1601< 0.1%
 

WBC_min
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct292
Distinct (%)7.4%
Missing73
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean10.36440794
Minimum0.1
Maximum128.3
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:18.569843image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile3.7
Q17.2
median9.5
Q312.6
95-th percentile19.3
Maximum128.3
Range128.2
Interquartile range (IQR)5.4

Descriptive statistics

Standard deviation5.554034813
Coefficient of variation (CV)0.5358757434
Kurtosis62.2377119
Mean10.36440794
Median Absolute Deviation (MAD)2.6
Skewness4.324614782
Sum40701.03
Variance30.8473027
MonotocityNot monotonic
2020-09-17T09:53:18.819551image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
9.4531.3%
 
7.7501.2%
 
8.8491.2%
 
9.2491.2%
 
10.3481.2%
 
6.9481.2%
 
9.7471.2%
 
7.4471.2%
 
9.9471.2%
 
10.4461.1%
 
Other values (282)344386.1%
 
(Missing)731.8%
 
ValueCountFrequency (%) 
0.170.2%
 
0.290.2%
 
0.330.1%
 
0.430.1%
 
0.61< 0.1%
 
ValueCountFrequency (%) 
128.31< 0.1%
 
70.41< 0.1%
 
63.21< 0.1%
 
57.71< 0.1%
 
50.31< 0.1%
 

WBC_max
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct388
Distinct (%)9.9%
Missing73
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean14.95212121
Minimum0.1
Maximum187.5
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:19.062332image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile6.1
Q110.2
median13.5
Q318.1
95-th percentile27.37
Maximum187.5
Range187.4
Interquartile range (IQR)7.9

Descriptive statistics

Standard deviation8.25589462
Coefficient of variation (CV)0.5521554101
Kurtosis77.55514929
Mean14.95212121
Median Absolute Deviation (MAD)3.8
Skewness5.245211161
Sum58716.98
Variance68.15979598
MonotocityNot monotonic
2020-09-17T09:53:19.296815image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
11.4380.9%
 
10.3380.9%
 
11.9370.9%
 
11.7370.9%
 
12.3360.9%
 
11.8350.9%
 
10.9350.9%
 
13.7340.9%
 
13.2340.9%
 
12.7330.8%
 
Other values (378)357089.2%
 
(Missing)731.8%
 
ValueCountFrequency (%) 
0.150.1%
 
0.230.1%
 
0.330.1%
 
0.420.1%
 
0.51< 0.1%
 
ValueCountFrequency (%) 
187.51< 0.1%
 
155.61< 0.1%
 
95.41< 0.1%
 
88.91< 0.1%
 
76.71< 0.1%
 

WBC_med
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct600
Distinct (%)15.3%
Missing73
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean12.42723708
Minimum0.1
Maximum140.8
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:19.730825image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile5.1
Q18.7
median11.4
Q314.9
95-th percentile22.585
Maximum140.8
Range140.7
Interquartile range (IQR)6.2

Descriptive statistics

Standard deviation6.614503917
Coefficient of variation (CV)0.5322586087
Kurtosis75.94779277
Mean12.42723708
Median Absolute Deviation (MAD)3.1
Skewness5.207052583
Sum48801.76
Variance43.75166207
MonotocityNot monotonic
2020-09-17T09:53:19.951786image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
9.1380.9%
 
13.1360.9%
 
9.4360.9%
 
11.1350.9%
 
10.2340.9%
 
10.7330.8%
 
11.6320.8%
 
9.2310.8%
 
11.4310.8%
 
9310.8%
 
Other values (590)359089.8%
 
(Missing)731.8%
 
ValueCountFrequency (%) 
0.170.2%
 
0.220.1%
 
0.2530.1%
 
0.31< 0.1%
 
0.31< 0.1%
 
ValueCountFrequency (%) 
140.81< 0.1%
 
135.41< 0.1%
 
88.651< 0.1%
 
72.351< 0.1%
 
60.51< 0.1%
 

pH_min
Real number (ℝ≥0)

MISSING

Distinct67
Distinct (%)2.2%
Missing962
Missing (%)24.1%
Infinite0
Infinite (%)0.0%
Mean7.31614549
Minimum6.82
Maximum7.63
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:20.217608image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum6.82
5-th percentile7.15
Q17.27
median7.32
Q37.38
95-th percentile7.44
Maximum7.63
Range0.81
Interquartile range (IQR)0.11

Descriptive statistics

Standard deviation0.09012551832
Coefficient of variation (CV)0.01231871597
Kurtosis1.94185649
Mean7.31614549
Median Absolute Deviation (MAD)0.05
Skewness-0.857761357
Sum22226.45
Variance0.008122609053
MonotocityNot monotonic
2020-09-17T09:53:20.470379image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
7.361634.1%
 
7.31584.0%
 
7.321573.9%
 
7.351523.8%
 
7.341493.7%
 
7.371403.5%
 
7.381343.4%
 
7.311333.3%
 
7.281313.3%
 
7.331303.2%
 
Other values (57)159139.8%
 
(Missing)96224.1%
 
ValueCountFrequency (%) 
6.821< 0.1%
 
6.861< 0.1%
 
6.871< 0.1%
 
6.881< 0.1%
 
6.9120.1%
 
ValueCountFrequency (%) 
7.631< 0.1%
 
7.5720.1%
 
7.5620.1%
 
7.5520.1%
 
7.5320.1%
 

pH_max
Real number (ℝ≥0)

MISSING

Distinct49
Distinct (%)1.6%
Missing962
Missing (%)24.1%
Infinite0
Infinite (%)0.0%
Mean7.439881501
Minimum7.13
Maximum7.69
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:20.745194image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum7.13
5-th percentile7.33
Q17.41
median7.44
Q37.48
95-th percentile7.53
Maximum7.69
Range0.56
Interquartile range (IQR)0.07

Descriptive statistics

Standard deviation0.06090459929
Coefficient of variation (CV)0.008186232439
Kurtosis1.247493475
Mean7.439881501
Median Absolute Deviation (MAD)0.04
Skewness-0.3591066337
Sum22602.36
Variance0.003709370214
MonotocityNot monotonic
2020-09-17T09:53:20.963492image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%) 
7.442345.9%
 
7.462305.8%
 
7.452245.6%
 
7.432145.3%
 
7.421985.0%
 
7.481964.9%
 
7.471784.5%
 
7.411704.2%
 
7.41543.9%
 
7.491473.7%
 
Other values (39)109327.3%
 
(Missing)96224.1%
 
ValueCountFrequency (%) 
7.131< 0.1%
 
7.1520.1%
 
7.161< 0.1%
 
7.21< 0.1%
 
7.2220.1%
 
ValueCountFrequency (%) 
7.691< 0.1%
 
7.661< 0.1%
 
7.641< 0.1%
 
7.6350.1%
 
7.6240.1%
 

pH_med
Real number (ℝ≥0)

MISSING

Distinct105
Distinct (%)3.5%
Missing962
Missing (%)24.1%
Infinite0
Infinite (%)0.0%
Mean7.384957209
Minimum7.105
Maximum7.63
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:21.204244image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum7.105
5-th percentile7.28
Q17.35
median7.39
Q37.42
95-th percentile7.47
Maximum7.63
Range0.525
Interquartile range (IQR)0.07

Descriptive statistics

Standard deviation0.05726390863
Coefficient of variation (CV)0.007754128698
Kurtosis1.354087236
Mean7.384957209
Median Absolute Deviation (MAD)0.035
Skewness-0.5281266959
Sum22435.5
Variance0.003279155231
MonotocityNot monotonic
2020-09-17T09:53:21.458374image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
7.41995.0%
 
7.381674.2%
 
7.421654.1%
 
7.411594.0%
 
7.371483.7%
 
7.391393.5%
 
7.431373.4%
 
7.361323.3%
 
7.351233.1%
 
7.341042.6%
 
Other values (95)156539.1%
 
(Missing)96224.1%
 
ValueCountFrequency (%) 
7.1051< 0.1%
 
7.121< 0.1%
 
7.1351< 0.1%
 
7.141< 0.1%
 
7.1520.1%
 
ValueCountFrequency (%) 
7.631< 0.1%
 
7.581< 0.1%
 
7.5751< 0.1%
 
7.5720.1%
 
7.561< 0.1%
 

PaCO2_min
Real number (ℝ≥0)

MISSING

Distinct66
Distinct (%)2.2%
Missing977
Missing (%)24.4%
Infinite0
Infinite (%)0.0%
Mean34.57171684
Minimum0.3
Maximum95
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:21.706819image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile24
Q130
median34
Q338
95-th percentile48
Maximum95
Range94.7
Interquartile range (IQR)8

Descriptive statistics

Standard deviation7.830635923
Coefficient of variation (CV)0.2265041091
Kurtosis6.19729571
Mean34.57171684
Median Absolute Deviation (MAD)4
Skewness1.414228323
Sum104510.3
Variance61.31885896
MonotocityNot monotonic
2020-09-17T09:53:21.949823image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
352135.3%
 
362015.0%
 
342005.0%
 
321934.8%
 
301874.7%
 
311804.5%
 
331734.3%
 
381533.8%
 
291513.8%
 
371423.5%
 
Other values (56)123030.8%
 
(Missing)97724.4%
 
ValueCountFrequency (%) 
0.31< 0.1%
 
111< 0.1%
 
1220.1%
 
1420.1%
 
1530.1%
 
ValueCountFrequency (%) 
951< 0.1%
 
881< 0.1%
 
841< 0.1%
 
811< 0.1%
 
7920.1%
 

PaCO2_max
Real number (ℝ≥0)

MISSING

Distinct81
Distinct (%)2.7%
Missing977
Missing (%)24.4%
Infinite0
Infinite (%)0.0%
Mean47.49718822
Minimum16
Maximum100
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:22.196743image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile33
Q140
median46
Q352
95-th percentile68
Maximum100
Range84
Interquartile range (IQR)12

Descriptive statistics

Standard deviation11.17855986
Coefficient of variation (CV)0.2353520341
Kurtosis2.934372873
Mean47.49718822
Median Absolute Deviation (MAD)6
Skewness1.252508369
Sum143584
Variance124.9602006
MonotocityNot monotonic
2020-09-17T09:53:22.429178image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
431513.8%
 
461433.6%
 
451393.5%
 
441343.4%
 
481343.4%
 
491333.3%
 
401323.3%
 
411263.1%
 
421263.1%
 
471182.9%
 
Other values (71)168742.2%
 
(Missing)97724.4%
 
ValueCountFrequency (%) 
161< 0.1%
 
171< 0.1%
 
191< 0.1%
 
201< 0.1%
 
211< 0.1%
 
ValueCountFrequency (%) 
10020.1%
 
9820.1%
 
971< 0.1%
 
951< 0.1%
 
9430.1%
 

PaCO2_med
Real number (ℝ≥0)

MISSING

Distinct118
Distinct (%)3.9%
Missing977
Missing (%)24.4%
Infinite0
Infinite (%)0.0%
Mean40.06996361
Minimum14
Maximum99
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:22.679804image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile30
Q136
median39
Q343
95-th percentile53
Maximum99
Range85
Interquartile range (IQR)7

Descriptive statistics

Standard deviation7.814780763
Coefficient of variation (CV)0.1950283968
Kurtosis7.000374234
Mean40.06996361
Median Absolute Deviation (MAD)4
Skewness1.622596319
Sum121131.5
Variance61.07079838
MonotocityNot monotonic
2020-09-17T09:53:22.919338image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
371804.5%
 
391674.2%
 
401644.1%
 
381553.9%
 
361533.8%
 
411453.6%
 
421233.1%
 
441182.9%
 
341182.9%
 
431142.9%
 
Other values (108)158639.6%
 
(Missing)97724.4%
 
ValueCountFrequency (%) 
141< 0.1%
 
161< 0.1%
 
171< 0.1%
 
1920.1%
 
201< 0.1%
 
ValueCountFrequency (%) 
991< 0.1%
 
92.51< 0.1%
 
91.51< 0.1%
 
901< 0.1%
 
891< 0.1%
 

PaO2_min
Real number (ℝ≥0)

MISSING

Distinct224
Distinct (%)7.4%
Missing977
Missing (%)24.4%
Infinite0
Infinite (%)0.0%
Mean92.28761826
Minimum0
Maximum500
Zeros1
Zeros (%)< 0.1%
Memory size31.2 KiB
2020-09-17T09:53:23.316562image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile51
Q167
median81
Q3106
95-th percentile163.9
Maximum500
Range500
Interquartile range (IQR)39

Descriptive statistics

Standard deviation42.64195837
Coefficient of variation (CV)0.4620550315
Kurtosis17.87343645
Mean92.28761826
Median Absolute Deviation (MAD)18
Skewness3.104369612
Sum278985.47
Variance1818.336613
MonotocityNot monotonic
2020-09-17T09:53:23.557571image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
73721.8%
 
74631.6%
 
76601.5%
 
63601.5%
 
61601.5%
 
78591.5%
 
69571.4%
 
62571.4%
 
75561.4%
 
67541.4%
 
Other values (214)242560.6%
 
(Missing)97724.4%
 
ValueCountFrequency (%) 
01< 0.1%
 
7.471< 0.1%
 
2030.1%
 
221< 0.1%
 
231< 0.1%
 
ValueCountFrequency (%) 
5001< 0.1%
 
4601< 0.1%
 
4541< 0.1%
 
4461< 0.1%
 
4431< 0.1%
 

PaO2_max
Real number (ℝ≥0)

MISSING

Distinct455
Distinct (%)15.1%
Missing977
Missing (%)24.4%
Infinite0
Infinite (%)0.0%
Mean249.8511413
Minimum26
Maximum500
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:23.799403image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile84
Q1144
median228
Q3355
95-th percentile458
Maximum500
Range474
Interquartile range (IQR)211

Descriptive statistics

Standard deviation122.0523927
Coefficient of variation (CV)0.4885004412
Kurtosis-1.118289864
Mean249.8511413
Median Absolute Deviation (MAD)100
Skewness0.3128122044
Sum755300
Variance14896.78657
MonotocityNot monotonic
2020-09-17T09:53:24.033995image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
183190.5%
 
178170.4%
 
93170.4%
 
118170.4%
 
122160.4%
 
144160.4%
 
191150.4%
 
119150.4%
 
133150.4%
 
179150.4%
 
Other values (445)286171.5%
 
(Missing)97724.4%
 
ValueCountFrequency (%) 
261< 0.1%
 
271< 0.1%
 
301< 0.1%
 
341< 0.1%
 
3620.1%
 
ValueCountFrequency (%) 
50030.1%
 
4991< 0.1%
 
49830.1%
 
49730.1%
 
49640.1%
 

PaO2_med
Real number (ℝ≥0)

MISSING

Distinct470
Distinct (%)15.5%
Missing977
Missing (%)24.4%
Infinite0
Infinite (%)0.0%
Mean133.9596427
Minimum26
Maximum500
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:24.271737image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile73
Q196
median121
Q3158
95-th percentile235
Maximum500
Range474
Interquartile range (IQR)62

Descriptive statistics

Standard deviation55.07525258
Coefficient of variation (CV)0.4111331701
Kurtosis4.973214065
Mean133.9596427
Median Absolute Deviation (MAD)29
Skewness1.753371075
Sum404960
Variance3033.283447
MonotocityNot monotonic
2020-09-17T09:53:24.514225image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
117350.9%
 
93340.9%
 
110340.9%
 
104340.9%
 
90310.8%
 
99300.8%
 
94290.7%
 
126280.7%
 
111280.7%
 
96280.7%
 
Other values (460)271267.8%
 
(Missing)97724.4%
 
ValueCountFrequency (%) 
261< 0.1%
 
26.51< 0.1%
 
271< 0.1%
 
341< 0.1%
 
3620.1%
 
ValueCountFrequency (%) 
5001< 0.1%
 
4601< 0.1%
 
4541< 0.1%
 
4461< 0.1%
 
4431< 0.1%
 

DiasABP_min
Real number (ℝ≥0)

MISSING

Distinct80
Distinct (%)2.9%
Missing1221
Missing (%)30.5%
Infinite0
Infinite (%)0.0%
Mean44.49406261
Minimum1
Maximum97
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:24.771793image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile30
Q138
median44
Q350
95-th percentile61
Maximum97
Range96
Interquartile range (IQR)12

Descriptive statistics

Standard deviation10.05694105
Coefficient of variation (CV)0.2260288331
Kurtosis1.869034722
Mean44.49406261
Median Absolute Deviation (MAD)6
Skewness0.361454326
Sum123649
Variance101.1420634
MonotocityNot monotonic
2020-09-17T09:53:25.012082image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
421483.7%
 
441433.6%
 
401423.5%
 
461223.0%
 
431223.0%
 
411162.9%
 
451152.9%
 
391102.8%
 
481082.7%
 
471052.6%
 
Other values (70)154838.7%
 
(Missing)122130.5%
 
ValueCountFrequency (%) 
11< 0.1%
 
21< 0.1%
 
31< 0.1%
 
420.1%
 
51< 0.1%
 
ValueCountFrequency (%) 
971< 0.1%
 
941< 0.1%
 
911< 0.1%
 
8820.1%
 
811< 0.1%
 

DiasABP_max
Real number (ℝ≥0)

MISSING

Distinct126
Distinct (%)4.5%
Missing1221
Missing (%)30.5%
Infinite0
Infinite (%)0.0%
Mean83.95106153
Minimum22
Maximum268
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:25.248740image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile61
Q172
median81
Q392
95-th percentile116
Maximum268
Range246
Interquartile range (IQR)20

Descriptive statistics

Standard deviation19.16185977
Coefficient of variation (CV)0.228250357
Kurtosis14.61128272
Mean83.95106153
Median Absolute Deviation (MAD)10
Skewness2.451345822
Sum233300
Variance367.1768698
MonotocityNot monotonic
2020-09-17T09:53:25.487696image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
79942.4%
 
75882.2%
 
77852.1%
 
80842.1%
 
76812.0%
 
83802.0%
 
87792.0%
 
72751.9%
 
86731.8%
 
78721.8%
 
Other values (116)196849.2%
 
(Missing)122130.5%
 
ValueCountFrequency (%) 
221< 0.1%
 
321< 0.1%
 
351< 0.1%
 
381< 0.1%
 
4420.1%
 
ValueCountFrequency (%) 
26820.1%
 
2581< 0.1%
 
2391< 0.1%
 
22820.1%
 
2151< 0.1%
 

DiasABP_med
Real number (ℝ≥0)

MISSING

Distinct118
Distinct (%)4.2%
Missing1221
Missing (%)30.5%
Infinite0
Infinite (%)0.0%
Mean59.59247931
Minimum22
Maximum104
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:25.735732image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum22
5-th percentile46
Q153
median58
Q365.5
95-th percentile78
Maximum104
Range82
Interquartile range (IQR)12.5

Descriptive statistics

Standard deviation9.578607615
Coefficient of variation (CV)0.1607351754
Kurtosis0.7188102887
Mean59.59247931
Median Absolute Deviation (MAD)6
Skewness0.5994898541
Sum165607.5
Variance91.74972384
MonotocityNot monotonic
2020-09-17T09:53:25.959455image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
541193.0%
 
571152.9%
 
591132.8%
 
561112.8%
 
581102.8%
 
531062.6%
 
551052.6%
 
60972.4%
 
52902.2%
 
61882.2%
 
Other values (108)172543.1%
 
(Missing)122130.5%
 
ValueCountFrequency (%) 
221< 0.1%
 
3020.1%
 
321< 0.1%
 
32.51< 0.1%
 
3520.1%
 
ValueCountFrequency (%) 
1041< 0.1%
 
9720.1%
 
961< 0.1%
 
951< 0.1%
 
9420.1%
 

FiO2_min
Real number (ℝ≥0)

MISSING

Distinct26
Distinct (%)1.0%
Missing1283
Missing (%)32.1%
Infinite0
Infinite (%)0.0%
Mean0.4401361796
Minimum0.21
Maximum1
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:26.173780image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.21
5-th percentile0.35
Q10.4
median0.4
Q30.5
95-th percentile0.6
Maximum1
Range0.79
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.1040183965
Coefficient of variation (CV)0.2363323019
Kurtosis12.30193609
Mean0.4401361796
Median Absolute Deviation (MAD)0
Skewness2.918772088
Sum1195.85
Variance0.01081982681
MonotocityNot monotonic
2020-09-17T09:53:26.393356image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%) 
0.4153238.3%
 
0.570217.5%
 
0.351804.5%
 
0.3902.2%
 
0.6792.0%
 
1391.0%
 
0.7360.9%
 
0.8110.3%
 
0.2880.2%
 
0.4580.2%
 
Other values (16)320.8%
 
(Missing)128332.1%
 
ValueCountFrequency (%) 
0.2160.1%
 
0.221< 0.1%
 
0.2430.1%
 
0.251< 0.1%
 
0.2880.2%
 
ValueCountFrequency (%) 
1391.0%
 
0.981< 0.1%
 
0.961< 0.1%
 
0.9530.1%
 
0.930.1%
 

FiO2_max
Real number (ℝ≥0)

MISSING

Distinct21
Distinct (%)0.8%
Missing1283
Missing (%)32.1%
Infinite0
Infinite (%)0.0%
Mean0.8394965035
Minimum0.28
Maximum1
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:26.618597image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.28
5-th percentile0.4
Q10.6
median1
Q31
95-th percentile1
Maximum1
Range0.72
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.228971459
Coefficient of variation (CV)0.2727485558
Kurtosis-0.9879125269
Mean0.8394965035
Median Absolute Deviation (MAD)0
Skewness-0.8755506476
Sum2280.912
Variance0.05242792902
MonotocityNot monotonic
2020-09-17T09:53:26.957785image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%) 
1174743.7%
 
0.540210.1%
 
0.61694.2%
 
0.41463.6%
 
0.71343.4%
 
0.8461.1%
 
0.35270.7%
 
0.95190.5%
 
0.360.1%
 
0.4540.1%
 
Other values (11)170.4%
 
(Missing)128332.1%
 
ValueCountFrequency (%) 
0.281< 0.1%
 
0.360.1%
 
0.311< 0.1%
 
0.35270.7%
 
0.3521< 0.1%
 
ValueCountFrequency (%) 
1174743.7%
 
0.981< 0.1%
 
0.9620.1%
 
0.95190.5%
 
0.930.1%
 

FiO2_med
Real number (ℝ≥0)

MISSING

Distinct34
Distinct (%)1.3%
Missing1283
Missing (%)32.1%
Infinite0
Infinite (%)0.0%
Mean0.5075524476
Minimum0.28
Maximum1
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:27.183592image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.28
5-th percentile0.4
Q10.4
median0.5
Q30.5
95-th percentile0.8
Maximum1
Range0.72
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.1396489573
Coefficient of variation (CV)0.2751419248
Kurtosis3.548116812
Mean0.5075524476
Median Absolute Deviation (MAD)0.1
Skewness1.787008009
Sum1379.02
Variance0.01950183129
MonotocityNot monotonic
2020-09-17T09:53:27.396051image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%) 
0.598524.6%
 
0.489322.3%
 
0.62406.0%
 
0.71343.4%
 
1832.1%
 
0.45812.0%
 
0.35631.6%
 
0.55511.3%
 
0.8380.9%
 
0.3370.9%
 
Other values (24)1122.8%
 
(Missing)128332.1%
 
ValueCountFrequency (%) 
0.2830.1%
 
0.291< 0.1%
 
0.3370.9%
 
0.321< 0.1%
 
0.3251< 0.1%
 
ValueCountFrequency (%) 
1832.1%
 
0.981< 0.1%
 
0.9751< 0.1%
 
0.95160.4%
 
0.960.1%
 

MAP_min
Real number (ℝ≥0)

MISSING

Distinct115
Distinct (%)4.1%
Missing1208
Missing (%)30.2%
Infinite0
Infinite (%)0.0%
Mean58.52901146
Minimum1
Maximum265
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:27.631861image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile38
Q152
median58
Q365
95-th percentile80
Maximum265
Range264
Interquartile range (IQR)13

Descriptive statistics

Standard deviation16.5296781
Coefficient of variation (CV)0.2824185423
Kurtosis35.35293631
Mean58.52901146
Median Absolute Deviation (MAD)7
Skewness2.71273969
Sum163413
Variance273.230258
MonotocityNot monotonic
2020-09-17T09:53:27.881168image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
571273.2%
 
551243.1%
 
581193.0%
 
561142.9%
 
601132.8%
 
521122.8%
 
511022.5%
 
621022.5%
 
591012.5%
 
54992.5%
 
Other values (105)167942.0%
 
(Missing)120830.2%
 
ValueCountFrequency (%) 
140.1%
 
2100.2%
 
360.1%
 
440.1%
 
520.1%
 
ValueCountFrequency (%) 
2651< 0.1%
 
2581< 0.1%
 
2471< 0.1%
 
2361< 0.1%
 
2291< 0.1%
 

MAP_max
Real number (ℝ≥0)

MISSING

Distinct216
Distinct (%)7.7%
Missing1208
Missing (%)30.2%
Infinite0
Infinite (%)0.0%
Mean118.4319484
Minimum4
Maximum300
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:28.133169image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile84
Q198
median109
Q3126
95-th percentile198.45
Maximum300
Range296
Interquartile range (IQR)28

Descriptive statistics

Standard deviation36.71395365
Coefficient of variation (CV)0.3100004191
Kurtosis6.390362125
Mean118.4319484
Median Absolute Deviation (MAD)13
Skewness2.272971365
Sum330662
Variance1347.914393
MonotocityNot monotonic
2020-09-17T09:53:28.369706image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
102721.8%
 
103681.7%
 
100671.7%
 
104671.7%
 
98661.7%
 
95661.7%
 
105601.5%
 
99601.5%
 
110591.5%
 
107591.5%
 
Other values (206)214853.7%
 
(Missing)120830.2%
 
ValueCountFrequency (%) 
41< 0.1%
 
111< 0.1%
 
161< 0.1%
 
301< 0.1%
 
401< 0.1%
 
ValueCountFrequency (%) 
30020.1%
 
2991< 0.1%
 
29530.1%
 
29420.1%
 
2921< 0.1%
 

MAP_med
Real number (ℝ≥0)

MISSING

Distinct138
Distinct (%)4.9%
Missing1208
Missing (%)30.2%
Infinite0
Infinite (%)0.0%
Mean80.0730659
Minimum4
Maximum265
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:28.624945image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile65
Q172
median78
Q386
95-th percentile101
Maximum265
Range261
Interquartile range (IQR)14

Descriptive statistics

Standard deviation13.89570089
Coefficient of variation (CV)0.173537765
Kurtosis48.91325203
Mean80.0730659
Median Absolute Deviation (MAD)7
Skewness4.251444415
Sum223564
Variance193.0905032
MonotocityNot monotonic
2020-09-17T09:53:28.871664image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
731132.8%
 
761022.5%
 
74952.4%
 
77942.4%
 
70912.3%
 
75912.3%
 
71912.3%
 
78872.2%
 
69832.1%
 
81832.1%
 
Other values (128)186246.6%
 
(Missing)120830.2%
 
ValueCountFrequency (%) 
41< 0.1%
 
111< 0.1%
 
161< 0.1%
 
301< 0.1%
 
401< 0.1%
 
ValueCountFrequency (%) 
2651< 0.1%
 
260.51< 0.1%
 
258.51< 0.1%
 
2471< 0.1%
 
239.51< 0.1%
 

MechVent_min
Boolean

CONSTANT
REJECTED

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
0
4000 
ValueCountFrequency (%) 
04000100.0%
 
2020-09-17T09:53:29.038351image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
1
2529 
0
1471 
ValueCountFrequency (%) 
1252963.2%
 
0147136.8%
 
2020-09-17T09:53:29.096295image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size31.2 KiB
0
3999 
0.5
 
1
ValueCountFrequency (%) 
03999> 99.9%
 
0.51< 0.1%
 
2020-09-17T09:53:29.173944image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

SysABP_min
Real number (ℝ≥0)

MISSING

Distinct127
Distinct (%)4.6%
Missing1220
Missing (%)30.5%
Infinite0
Infinite (%)0.0%
Mean86.94856115
Minimum6
Maximum165
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:29.327995image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile60
Q176
median85
Q397
95-th percentile120
Maximum165
Range159
Interquartile range (IQR)21

Descriptive statistics

Standard deviation18.19766369
Coefficient of variation (CV)0.2092922925
Kurtosis1.680312462
Mean86.94856115
Median Absolute Deviation (MAD)10
Skewness0.2781287114
Sum241717
Variance331.1549637
MonotocityNot monotonic
2020-09-17T09:53:29.568380image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
85962.4%
 
80892.2%
 
88872.2%
 
89862.1%
 
83852.1%
 
90771.9%
 
86751.9%
 
81741.8%
 
75741.8%
 
91691.7%
 
Other values (117)196849.2%
 
(Missing)122030.5%
 
ValueCountFrequency (%) 
61< 0.1%
 
720.1%
 
81< 0.1%
 
1120.1%
 
121< 0.1%
 
ValueCountFrequency (%) 
1651< 0.1%
 
1541< 0.1%
 
15120.1%
 
1501< 0.1%
 
14920.1%
 

SysABP_max
Real number (ℝ≥0)

MISSING

Distinct163
Distinct (%)5.9%
Missing1220
Missing (%)30.5%
Infinite0
Infinite (%)0.0%
Mean159.1953237
Minimum7
Maximum295
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:29.832553image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile121
Q1141
median156
Q3175
95-th percentile207
Maximum295
Range288
Interquartile range (IQR)34

Descriptive statistics

Standard deviation26.87031842
Coefficient of variation (CV)0.1687883651
Kurtosis1.755483392
Mean159.1953237
Median Absolute Deviation (MAD)17
Skewness0.6678399322
Sum442563
Variance722.0140119
MonotocityNot monotonic
2020-09-17T09:53:30.064345image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
155571.4%
 
154531.3%
 
144521.3%
 
158501.2%
 
157501.2%
 
147481.2%
 
146471.2%
 
141471.2%
 
153471.2%
 
149461.1%
 
Other values (153)228357.1%
 
(Missing)122030.5%
 
ValueCountFrequency (%) 
71< 0.1%
 
521< 0.1%
 
821< 0.1%
 
851< 0.1%
 
941< 0.1%
 
ValueCountFrequency (%) 
2951< 0.1%
 
28420.1%
 
2821< 0.1%
 
2791< 0.1%
 
2751< 0.1%
 

SysABP_med
Real number (ℝ≥0)

MISSING

Distinct189
Distinct (%)6.8%
Missing1220
Missing (%)30.5%
Infinite0
Infinite (%)0.0%
Mean119.5147482
Minimum7
Maximum196
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:30.313357image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile96
Q1107
median117
Q3131
95-th percentile152
Maximum196
Range189
Interquartile range (IQR)24

Descriptive statistics

Standard deviation17.51032077
Coefficient of variation (CV)0.146511799
Kurtosis0.7922785846
Mean119.5147482
Median Absolute Deviation (MAD)11.5
Skewness0.4987567983
Sum332251
Variance306.6113333
MonotocityNot monotonic
2020-09-17T09:53:30.695699image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
108641.6%
 
113611.5%
 
102601.5%
 
117571.4%
 
110561.4%
 
107551.4%
 
112551.4%
 
105551.4%
 
120541.4%
 
104531.3%
 
Other values (179)221055.2%
 
(Missing)122030.5%
 
ValueCountFrequency (%) 
71< 0.1%
 
521< 0.1%
 
751< 0.1%
 
75.51< 0.1%
 
771< 0.1%
 
ValueCountFrequency (%) 
1961< 0.1%
 
185.51< 0.1%
 
1841< 0.1%
 
180.51< 0.1%
 
1791< 0.1%
 

SaO2_min
Real number (ℝ≥0)

MISSING

Distinct43
Distinct (%)2.4%
Missing2208
Missing (%)55.2%
Infinite0
Infinite (%)0.0%
Mean94.82779018
Minimum26
Maximum100
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:30.952465image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile87
Q194
median96
Q398
95-th percentile99
Maximum100
Range74
Interquartile range (IQR)4

Descriptive statistics

Standard deviation5.511473037
Coefficient of variation (CV)0.05812086337
Kurtosis42.37485665
Mean94.82779018
Median Absolute Deviation (MAD)2
Skewness-5.267224953
Sum169931.4
Variance30.37633504
MonotocityNot monotonic
2020-09-17T09:53:31.178038image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%) 
983799.5%
 
973799.5%
 
962305.8%
 
951724.3%
 
941614.0%
 
99882.2%
 
93872.2%
 
92741.8%
 
91491.2%
 
89250.6%
 
Other values (33)1483.7%
 
(Missing)220855.2%
 
ValueCountFrequency (%) 
261< 0.1%
 
331< 0.1%
 
381< 0.1%
 
461< 0.1%
 
511< 0.1%
 
ValueCountFrequency (%) 
10090.2%
 
99882.2%
 
983799.5%
 
973799.5%
 
962305.8%
 

SaO2_max
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct26
Distinct (%)1.5%
Missing2208
Missing (%)55.2%
Infinite0
Infinite (%)0.0%
Mean97.64397321
Minimum46
Maximum100
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:31.405604image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum46
5-th percentile94
Q198
median98
Q399
95-th percentile99
Maximum100
Range54
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.121658898
Coefficient of variation (CV)0.03196980618
Kurtosis118.8129442
Mean97.64397321
Median Absolute Deviation (MAD)1
Skewness-9.202579521
Sum174978
Variance9.744754277
MonotocityNot monotonic
2020-09-17T09:53:31.616083image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%) 
9871817.9%
 
9956414.1%
 
972325.8%
 
100741.8%
 
96681.7%
 
95431.1%
 
94250.6%
 
93190.5%
 
92120.3%
 
9190.2%
 
Other values (16)280.7%
 
(Missing)220855.2%
 
ValueCountFrequency (%) 
461< 0.1%
 
471< 0.1%
 
6020.1%
 
6720.1%
 
741< 0.1%
 
ValueCountFrequency (%) 
100741.8%
 
9956414.1%
 
9871817.9%
 
972325.8%
 
96681.7%
 

SaO2_med
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct42
Distinct (%)2.3%
Missing2208
Missing (%)55.2%
Infinite0
Infinite (%)0.0%
Mean96.76590402
Minimum40
Maximum100
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:31.893478image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile93
Q196.5
median97.5
Q398
95-th percentile99
Maximum100
Range60
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation3.361816891
Coefficient of variation (CV)0.03474175046
Kurtosis101.9280921
Mean96.76590402
Median Absolute Deviation (MAD)0.5
Skewness-8.267727337
Sum173404.5
Variance11.30181281
MonotocityNot monotonic
2020-09-17T09:53:32.117118image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%) 
9861215.3%
 
973759.4%
 
991584.0%
 
961533.8%
 
97.51102.8%
 
95681.7%
 
96.5671.7%
 
98.5511.3%
 
94401.0%
 
95.5260.7%
 
Other values (32)1323.3%
 
(Missing)220855.2%
 
ValueCountFrequency (%) 
401< 0.1%
 
461< 0.1%
 
6020.1%
 
6720.1%
 
73.51< 0.1%
 
ValueCountFrequency (%) 
10090.2%
 
99.51< 0.1%
 
991584.0%
 
98.5511.3%
 
9861215.3%
 

Albumin_min
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct39
Distinct (%)2.4%
Missing2385
Missing (%)59.6%
Infinite0
Infinite (%)0.0%
Mean2.915294118
Minimum1
Maximum5.3
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:32.362602image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.8
Q12.4
median2.9
Q33.4
95-th percentile4
Maximum5.3
Range4.3
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.6618813238
Coefficient of variation (CV)0.2270375808
Kurtosis-0.3570254869
Mean2.915294118
Median Absolute Deviation (MAD)0.5
Skewness0.01703631166
Sum4708.2
Variance0.4380868868
MonotocityNot monotonic
2020-09-17T09:53:32.582743image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%) 
3.11002.5%
 
3992.5%
 
2.7992.5%
 
2.8862.1%
 
2.9852.1%
 
2.5822.1%
 
3.4802.0%
 
2.6781.9%
 
3.2721.8%
 
3.3701.8%
 
Other values (29)76419.1%
 
(Missing)238559.6%
 
ValueCountFrequency (%) 
11< 0.1%
 
1.120.1%
 
1.220.1%
 
1.360.1%
 
1.440.1%
 
ValueCountFrequency (%) 
5.31< 0.1%
 
4.81< 0.1%
 
4.630.1%
 
4.530.1%
 
4.480.2%
 

Albumin_max
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct40
Distinct (%)2.5%
Missing2385
Missing (%)59.6%
Infinite0
Infinite (%)0.0%
Mean3.027430341
Minimum1.1
Maximum5.3
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:32.809807image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile2
Q12.6
median3
Q33.5
95-th percentile4.1
Maximum5.3
Range4.2
Interquartile range (IQR)0.9

Descriptive statistics

Standard deviation0.6382746822
Coefficient of variation (CV)0.2108305098
Kurtosis-0.2724589104
Mean3.027430341
Median Absolute Deviation (MAD)0.4
Skewness0.02844313336
Sum4889.3
Variance0.40739457
MonotocityNot monotonic
2020-09-17T09:53:33.031986image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%) 
2.71122.8%
 
3.11082.7%
 
3972.4%
 
2.9892.2%
 
2.8882.2%
 
3.2822.1%
 
3.4802.0%
 
3.3802.0%
 
2.6781.9%
 
2.5751.9%
 
Other values (30)72618.1%
 
(Missing)238559.6%
 
ValueCountFrequency (%) 
1.11< 0.1%
 
1.220.1%
 
1.320.1%
 
1.420.1%
 
1.560.1%
 
ValueCountFrequency (%) 
5.31< 0.1%
 
51< 0.1%
 
4.91< 0.1%
 
4.820.1%
 
4.650.1%
 

Albumin_med
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct75
Distinct (%)4.6%
Missing2385
Missing (%)59.6%
Infinite0
Infinite (%)0.0%
Mean2.973374613
Minimum1.1
Maximum5.3
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:33.278622image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile1.9
Q12.5
median3
Q33.4
95-th percentile4
Maximum5.3
Range4.2
Interquartile range (IQR)0.9

Descriptive statistics

Standard deviation0.6372790617
Coefficient of variation (CV)0.2143285474
Kurtosis-0.3352092688
Mean2.973374613
Median Absolute Deviation (MAD)0.5
Skewness0.03174930875
Sum4802
Variance0.4061246025
MonotocityNot monotonic
2020-09-17T09:53:33.498956image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
3.1932.3%
 
3902.2%
 
2.7892.2%
 
2.9842.1%
 
2.6802.0%
 
2.8781.9%
 
2.5711.8%
 
3.4701.8%
 
3.2681.7%
 
3.6681.7%
 
Other values (65)82420.6%
 
(Missing)238559.6%
 
ValueCountFrequency (%) 
1.11< 0.1%
 
1.1520.1%
 
1.21< 0.1%
 
1.31< 0.1%
 
1.430.1%
 
ValueCountFrequency (%) 
5.31< 0.1%
 
4.81< 0.1%
 
4.630.1%
 
4.530.1%
 
4.490.2%
 

ALP_min
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct268
Distinct (%)15.9%
Missing2310
Missing (%)57.8%
Infinite0
Infinite (%)0.0%
Mean98.33491124
Minimum12
Maximum1339
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:33.725014image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile36
Q155
median75
Q3105
95-th percentile231.1
Maximum1339
Range1327
Interquartile range (IQR)50

Descriptive statistics

Standard deviation95.31268053
Coefficient of variation (CV)0.9692659435
Kurtosis47.03655146
Mean98.33491124
Median Absolute Deviation (MAD)24
Skewness5.574342121
Sum166186
Variance9084.507069
MonotocityNot monotonic
2020-09-17T09:53:33.975834image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
61340.9%
 
48270.7%
 
47260.7%
 
80250.6%
 
73250.6%
 
65250.6%
 
63250.6%
 
46250.6%
 
66240.6%
 
56230.6%
 
Other values (258)143135.8%
 
(Missing)231057.8%
 
ValueCountFrequency (%) 
121< 0.1%
 
1320.1%
 
1920.1%
 
2030.1%
 
2140.1%
 
ValueCountFrequency (%) 
13391< 0.1%
 
12661< 0.1%
 
9471< 0.1%
 
8741< 0.1%
 
8621< 0.1%
 

ALP_max
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct290
Distinct (%)17.2%
Missing2310
Missing (%)57.8%
Infinite0
Infinite (%)0.0%
Mean111.3443787
Minimum12
Maximum2205
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:34.245640image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile39
Q159
median80.5
Q3115
95-th percentile274
Maximum2205
Range2193
Interquartile range (IQR)56

Descriptive statistics

Standard deviation125.7311584
Coefficient of variation (CV)1.129209754
Kurtosis82.65697359
Mean111.3443787
Median Absolute Deviation (MAD)25.5
Skewness7.252639812
Sum188172
Variance15808.3242
MonotocityNot monotonic
2020-09-17T09:53:34.625419image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
67270.7%
 
80260.7%
 
65250.6%
 
61250.6%
 
62250.6%
 
73240.6%
 
74220.5%
 
46220.5%
 
53220.5%
 
48210.5%
 
Other values (280)145136.3%
 
(Missing)231057.8%
 
ValueCountFrequency (%) 
121< 0.1%
 
1920.1%
 
201< 0.1%
 
2120.1%
 
2220.1%
 
ValueCountFrequency (%) 
22051< 0.1%
 
17521< 0.1%
 
15041< 0.1%
 
12041< 0.1%
 
10241< 0.1%
 

ALP_med
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct377
Distinct (%)22.3%
Missing2310
Missing (%)57.8%
Infinite0
Infinite (%)0.0%
Mean104.3597633
Minimum12
Maximum1501
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:34.874516image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile38
Q157
median78
Q3110
95-th percentile243.55
Maximum1501
Range1489
Interquartile range (IQR)53

Descriptive statistics

Standard deviation105.580245
Coefficient of variation (CV)1.011694945
Kurtosis48.57111942
Mean104.3597633
Median Absolute Deviation (MAD)24
Skewness5.703137505
Sum176368
Variance11147.18814
MonotocityNot monotonic
2020-09-17T09:53:35.113604image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
65240.6%
 
62230.6%
 
61230.6%
 
67220.5%
 
49220.5%
 
79210.5%
 
82210.5%
 
55210.5%
 
53200.5%
 
64200.5%
 
Other values (367)147336.8%
 
(Missing)231057.8%
 
ValueCountFrequency (%) 
121< 0.1%
 
1920.1%
 
201< 0.1%
 
2120.1%
 
2230.1%
 
ValueCountFrequency (%) 
15011< 0.1%
 
13991< 0.1%
 
1004.51< 0.1%
 
9601< 0.1%
 
9031< 0.1%
 

ALT_min
Real number (ℝ≥0)

MISSING

Distinct311
Distinct (%)18.1%
Missing2279
Missing (%)57.0%
Infinite0
Infinite (%)0.0%
Mean120.2963393
Minimum1
Maximum6558
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:35.374354image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q116
median29
Q362
95-th percentile393
Maximum6558
Range6557
Interquartile range (IQR)46

Descriptive statistics

Standard deviation424.5157041
Coefficient of variation (CV)3.528916229
Kurtosis93.593552
Mean120.2963393
Median Absolute Deviation (MAD)16
Skewness8.589556063
Sum207030
Variance180213.5831
MonotocityNot monotonic
2020-09-17T09:53:35.603714image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
17581.5%
 
15551.4%
 
13511.3%
 
14511.3%
 
18491.2%
 
16471.2%
 
11461.1%
 
12461.1%
 
19391.0%
 
20370.9%
 
Other values (301)124231.1%
 
(Missing)227957.0%
 
ValueCountFrequency (%) 
120.1%
 
340.1%
 
470.2%
 
590.2%
 
6110.3%
 
ValueCountFrequency (%) 
65581< 0.1%
 
64801< 0.1%
 
49401< 0.1%
 
42901< 0.1%
 
41001< 0.1%
 

ALT_max
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct375
Distinct (%)21.8%
Missing2279
Missing (%)57.0%
Infinite0
Infinite (%)0.0%
Mean222.4270773
Minimum3
Maximum11470
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:35.850911image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile10
Q118
median33
Q376
95-th percentile733
Maximum11470
Range11467
Interquartile range (IQR)58

Descriptive statistics

Standard deviation896.6995988
Coefficient of variation (CV)4.031431828
Kurtosis72.8257543
Mean222.4270773
Median Absolute Deviation (MAD)19
Skewness7.847079998
Sum382797
Variance804070.1704
MonotocityNot monotonic
2020-09-17T09:53:36.093307image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
17541.4%
 
14521.3%
 
18471.2%
 
16461.1%
 
13451.1%
 
20441.1%
 
15421.1%
 
12411.0%
 
26360.9%
 
11340.9%
 
Other values (365)128032.0%
 
(Missing)227957.0%
 
ValueCountFrequency (%) 
320.1%
 
450.1%
 
570.2%
 
6100.2%
 
7110.3%
 
ValueCountFrequency (%) 
114701< 0.1%
 
110301< 0.1%
 
104701< 0.1%
 
104401< 0.1%
 
92601< 0.1%
 

ALT_med
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct445
Distinct (%)25.9%
Missing2279
Missing (%)57.0%
Infinite0
Infinite (%)0.0%
Mean172.7513074
Minimum3
Maximum9890
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:36.353000image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile9
Q117
median31
Q369
95-th percentile529.5
Maximum9890
Range9887
Interquartile range (IQR)52

Descriptive statistics

Standard deviation666.4077758
Coefficient of variation (CV)3.857613502
Kurtosis85.01531446
Mean172.7513074
Median Absolute Deviation (MAD)17
Skewness8.257738911
Sum297305
Variance444099.3236
MonotocityNot monotonic
2020-09-17T09:53:36.607159image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
17501.2%
 
14471.2%
 
16441.1%
 
12431.1%
 
15431.1%
 
18431.1%
 
13401.0%
 
20380.9%
 
11370.9%
 
19340.9%
 
Other values (435)130232.6%
 
(Missing)227957.0%
 
ValueCountFrequency (%) 
330.1%
 
3.51< 0.1%
 
450.1%
 
4.51< 0.1%
 
560.1%
 
ValueCountFrequency (%) 
98901< 0.1%
 
9052.51< 0.1%
 
8907.51< 0.1%
 
6391.51< 0.1%
 
6147.51< 0.1%
 

AST_min
Real number (ℝ≥0)

MISSING

Distinct353
Distinct (%)20.5%
Missing2275
Missing (%)56.9%
Infinite0
Infinite (%)0.0%
Mean143.8104348
Minimum4
Maximum15680
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:36.858290image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile14
Q124
median41
Q386
95-th percentile476.4
Maximum15680
Range15676
Interquartile range (IQR)62

Descriptive statistics

Standard deviation535.6919604
Coefficient of variation (CV)3.724986725
Kurtosis426.6306693
Mean143.8104348
Median Absolute Deviation (MAD)22
Skewness16.81977039
Sum248073
Variance286965.8765
MonotocityNot monotonic
2020-09-17T09:53:37.110837image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
17471.2%
 
20421.1%
 
24411.0%
 
23350.9%
 
16350.9%
 
15340.9%
 
22340.9%
 
18330.8%
 
21320.8%
 
28310.8%
 
Other values (343)136134.0%
 
(Missing)227556.9%
 
ValueCountFrequency (%) 
41< 0.1%
 
51< 0.1%
 
61< 0.1%
 
740.1%
 
81< 0.1%
 
ValueCountFrequency (%) 
156801< 0.1%
 
50711< 0.1%
 
48811< 0.1%
 
41541< 0.1%
 
40861< 0.1%
 

AST_max
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct432
Distinct (%)25.0%
Missing2275
Missing (%)56.9%
Infinite0
Infinite (%)0.0%
Mean348.2
Minimum6
Maximum18430
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:37.393850image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile15
Q126
median49
Q3119
95-th percentile1125.2
Maximum18430
Range18424
Interquartile range (IQR)93

Descriptive statistics

Standard deviation1408.873938
Coefficient of variation (CV)4.046162946
Kurtosis61.5535133
Mean348.2
Median Absolute Deviation (MAD)29
Skewness7.322958237
Sum600645
Variance1984925.773
MonotocityNot monotonic
2020-09-17T09:53:37.638904image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
20411.0%
 
17401.0%
 
24391.0%
 
18350.9%
 
25320.8%
 
28320.8%
 
26300.8%
 
22270.7%
 
27270.7%
 
15270.7%
 
Other values (422)139534.9%
 
(Missing)227556.9%
 
ValueCountFrequency (%) 
61< 0.1%
 
740.1%
 
81< 0.1%
 
940.1%
 
1090.2%
 
ValueCountFrequency (%) 
184301< 0.1%
 
160401< 0.1%
 
156801< 0.1%
 
132601< 0.1%
 
126801< 0.1%
 

AST_med
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct496
Distinct (%)28.8%
Missing2275
Missing (%)56.9%
Infinite0
Infinite (%)0.0%
Mean236.7263768
Minimum6
Maximum15680
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:37.909410image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile15
Q125
median46
Q3102.5
95-th percentile765
Maximum15680
Range15674
Interquartile range (IQR)77.5

Descriptive statistics

Standard deviation912.9149499
Coefficient of variation (CV)3.856414153
Kurtosis101.9525509
Mean236.7263768
Median Absolute Deviation (MAD)26
Skewness8.868381916
Sum408353
Variance833413.7058
MonotocityNot monotonic
2020-09-17T09:53:38.302780image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
20411.0%
 
17401.0%
 
24380.9%
 
15300.8%
 
18290.7%
 
25290.7%
 
22290.7%
 
26280.7%
 
27270.7%
 
29250.6%
 
Other values (486)140935.2%
 
(Missing)227556.9%
 
ValueCountFrequency (%) 
61< 0.1%
 
6.51< 0.1%
 
740.1%
 
950.1%
 
1080.2%
 
ValueCountFrequency (%) 
156801< 0.1%
 
129451< 0.1%
 
115701< 0.1%
 
81611< 0.1%
 
7675.51< 0.1%
 

Bilirubin_min
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct128
Distinct (%)7.5%
Missing2282
Missing (%)57.0%
Infinite0
Infinite (%)0.0%
Mean1.690628638
Minimum0.1
Maximum45.5
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:38.578050image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.2
Q10.4
median0.6
Q31.2
95-th percentile6.515
Maximum45.5
Range45.4
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation4.10186536
Coefficient of variation (CV)2.426236766
Kurtosis42.79865467
Mean1.690628638
Median Absolute Deviation (MAD)0.3
Skewness6.000069969
Sum2904.5
Variance16.82529943
MonotocityNot monotonic
2020-09-17T09:53:38.808125image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.32135.3%
 
0.41914.8%
 
0.51794.5%
 
0.61373.4%
 
0.21253.1%
 
0.71132.8%
 
0.8892.2%
 
0.9731.8%
 
1601.5%
 
1.2471.2%
 
Other values (118)49112.3%
 
(Missing)228257.0%
 
ValueCountFrequency (%) 
0.1310.8%
 
0.21253.1%
 
0.32135.3%
 
0.41914.8%
 
0.51794.5%
 
ValueCountFrequency (%) 
45.520.1%
 
40.71< 0.1%
 
391< 0.1%
 
34.71< 0.1%
 
34.31< 0.1%
 

Bilirubin_max
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct146
Distinct (%)8.5%
Missing2282
Missing (%)57.0%
Infinite0
Infinite (%)0.0%
Mean2.134575087
Minimum0.1
Maximum47.7
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:39.046717image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.2
Q10.4
median0.7
Q31.575
95-th percentile9.1
Maximum47.7
Range47.6
Interquartile range (IQR)1.175

Descriptive statistics

Standard deviation4.859546575
Coefficient of variation (CV)2.276587319
Kurtosis33.39138597
Mean2.134575087
Median Absolute Deviation (MAD)0.4
Skewness5.332764419
Sum3667.2
Variance23.61519292
MonotocityNot monotonic
2020-09-17T09:53:39.297008image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.31654.1%
 
0.41644.1%
 
0.51624.0%
 
0.61413.5%
 
0.71253.1%
 
0.8942.4%
 
0.2932.3%
 
0.9771.9%
 
1721.8%
 
1.1511.3%
 
Other values (136)57414.3%
 
(Missing)228257.0%
 
ValueCountFrequency (%) 
0.1180.4%
 
0.2932.3%
 
0.31654.1%
 
0.41644.1%
 
0.51624.0%
 
ValueCountFrequency (%) 
47.71< 0.1%
 
46.91< 0.1%
 
45.81< 0.1%
 
41.91< 0.1%
 
40.71< 0.1%
 

Bilirubin_med
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct192
Distinct (%)11.2%
Missing2282
Missing (%)57.0%
Infinite0
Infinite (%)0.0%
Mean1.888416764
Minimum0.1
Maximum45.9
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:39.536388image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.2
Q10.4
median0.7
Q31.4
95-th percentile7.6
Maximum45.9
Range45.8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation4.405599794
Coefficient of variation (CV)2.332959482
Kurtosis38.00279989
Mean1.888416764
Median Absolute Deviation (MAD)0.35
Skewness5.671895741
Sum3244.3
Variance19.40930955
MonotocityNot monotonic
2020-09-17T09:53:39.776940image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.31513.8%
 
0.41513.8%
 
0.51463.6%
 
0.61303.2%
 
0.7992.5%
 
0.2962.4%
 
0.8872.2%
 
0.9661.7%
 
1601.5%
 
1.2360.9%
 
Other values (182)69617.4%
 
(Missing)228257.0%
 
ValueCountFrequency (%) 
0.1200.5%
 
0.1530.1%
 
0.2962.4%
 
0.25140.4%
 
0.31513.8%
 
ValueCountFrequency (%) 
45.91< 0.1%
 
45.651< 0.1%
 
43.91< 0.1%
 
40.71< 0.1%
 
35.41< 0.1%
 

Lactate_min
Real number (ℝ≥0)

MISSING

Distinct87
Distinct (%)4.0%
Missing1817
Missing (%)45.4%
Infinite0
Infinite (%)0.0%
Mean1.70193312
Minimum0.3
Maximum21.2
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:40.016096image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.7
Q11.1
median1.4
Q31.9
95-th percentile3.6
Maximum21.2
Range20.9
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation1.187876245
Coefficient of variation (CV)0.697957065
Kurtosis46.19215047
Mean1.70193312
Median Absolute Deviation (MAD)0.4
Skewness4.760114043
Sum3715.32
Variance1.411049974
MonotocityNot monotonic
2020-09-17T09:53:40.238199image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.21794.5%
 
1.31654.1%
 
1.11553.9%
 
11483.7%
 
0.91453.6%
 
0.81253.1%
 
1.41233.1%
 
1.61223.0%
 
1.51142.9%
 
1.7902.2%
 
Other values (77)81720.4%
 
(Missing)181745.4%
 
ValueCountFrequency (%) 
0.31< 0.1%
 
0.440.1%
 
0.5160.4%
 
0.6411.0%
 
0.651< 0.1%
 
ValueCountFrequency (%) 
21.21< 0.1%
 
12.31< 0.1%
 
12.11< 0.1%
 
9.920.1%
 
9.41< 0.1%
 

Lactate_max
Real number (ℝ≥0)

MISSING

Distinct149
Distinct (%)6.8%
Missing1817
Missing (%)45.4%
Infinite0
Infinite (%)0.0%
Mean3.086862116
Minimum0.4
Maximum29.3
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:40.475504image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile0.9
Q11.5
median2.3
Q33.7
95-th percentile8
Maximum29.3
Range28.9
Interquartile range (IQR)2.2

Descriptive statistics

Standard deviation2.592516994
Coefficient of variation (CV)0.8398551333
Kurtosis13.81165812
Mean3.086862116
Median Absolute Deviation (MAD)0.9
Skewness2.976433306
Sum6738.62
Variance6.721144365
MonotocityNot monotonic
2020-09-17T09:53:40.702123image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.5912.3%
 
1.4882.2%
 
1.3882.2%
 
1.6862.1%
 
1.7812.0%
 
1.2802.0%
 
1.9792.0%
 
1.8761.9%
 
2671.7%
 
1.1641.6%
 
Other values (139)138334.6%
 
(Missing)181745.4%
 
ValueCountFrequency (%) 
0.420.1%
 
0.540.1%
 
0.660.1%
 
0.651< 0.1%
 
0.7100.2%
 
ValueCountFrequency (%) 
29.31< 0.1%
 
24.11< 0.1%
 
201< 0.1%
 
19.51< 0.1%
 
18.620.1%
 

Lactate_med
Real number (ℝ≥0)

MISSING

Distinct182
Distinct (%)8.3%
Missing1817
Missing (%)45.4%
Infinite0
Infinite (%)0.0%
Mean2.26102153
Minimum0.4
Maximum24.65
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:40.942138image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile0.9
Q11.3
median1.8
Q32.65
95-th percentile5.1
Maximum24.65
Range24.25
Interquartile range (IQR)1.35

Descriptive statistics

Standard deviation1.625227436
Coefficient of variation (CV)0.7188022821
Kurtosis27.28782429
Mean2.26102153
Median Absolute Deviation (MAD)0.6
Skewness3.785305719
Sum4935.81
Variance2.641364217
MonotocityNot monotonic
2020-09-17T09:53:41.180290image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
1.41032.6%
 
1.5972.4%
 
1.6932.3%
 
1.1922.3%
 
1.2912.3%
 
1.3892.2%
 
1.7761.9%
 
1.9741.8%
 
1651.6%
 
0.9631.6%
 
Other values (172)134033.5%
 
(Missing)181745.4%
 
ValueCountFrequency (%) 
0.420.1%
 
0.540.1%
 
0.5520.1%
 
0.660.1%
 
0.651< 0.1%
 
ValueCountFrequency (%) 
24.651< 0.1%
 
15.851< 0.1%
 
14.451< 0.1%
 
14.11< 0.1%
 
12.21< 0.1%
 

Cholesterol_min
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct140
Distinct (%)45.9%
Missing3695
Missing (%)92.4%
Infinite0
Infinite (%)0.0%
Mean156.3803279
Minimum28
Maximum330
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:41.421494image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile91
Q1123
median152
Q3188
95-th percentile229.4
Maximum330
Range302
Interquartile range (IQR)65

Descriptive statistics

Standard deviation46.31421154
Coefficient of variation (CV)0.296163924
Kurtosis0.7643540713
Mean156.3803279
Median Absolute Deviation (MAD)31
Skewness0.5220952003
Sum47696
Variance2145.006191
MonotocityNot monotonic
2020-09-17T09:53:41.795533image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
11180.2%
 
16450.1%
 
15450.1%
 
15540.1%
 
18940.1%
 
17540.1%
 
19940.1%
 
12740.1%
 
11740.1%
 
14940.1%
 
Other values (130)2596.5%
 
(Missing)369592.4%
 
ValueCountFrequency (%) 
281< 0.1%
 
401< 0.1%
 
591< 0.1%
 
681< 0.1%
 
731< 0.1%
 
ValueCountFrequency (%) 
3301< 0.1%
 
30720.1%
 
2971< 0.1%
 
2891< 0.1%
 
26320.1%
 

Cholesterol_max
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct139
Distinct (%)45.6%
Missing3695
Missing (%)92.4%
Infinite0
Infinite (%)0.0%
Mean156.7672131
Minimum28
Maximum330
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:42.047016image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile91
Q1123
median153
Q3188
95-th percentile229.4
Maximum330
Range302
Interquartile range (IQR)65

Descriptive statistics

Standard deviation46.39782465
Coefficient of variation (CV)0.2959663805
Kurtosis0.7230366409
Mean156.7672131
Median Absolute Deviation (MAD)32
Skewness0.5127912732
Sum47814
Variance2152.758132
MonotocityNot monotonic
2020-09-17T09:53:42.280216image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
11170.2%
 
16450.1%
 
15450.1%
 
11340.1%
 
18040.1%
 
16140.1%
 
15540.1%
 
10140.1%
 
18940.1%
 
19940.1%
 
Other values (129)2606.5%
 
(Missing)369592.4%
 
ValueCountFrequency (%) 
281< 0.1%
 
401< 0.1%
 
591< 0.1%
 
681< 0.1%
 
731< 0.1%
 
ValueCountFrequency (%) 
3301< 0.1%
 
30720.1%
 
2971< 0.1%
 
2891< 0.1%
 
26320.1%
 

Cholesterol_med
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct143
Distinct (%)46.9%
Missing3695
Missing (%)92.4%
Infinite0
Infinite (%)0.0%
Mean156.5737705
Minimum28
Maximum330
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:42.537234image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile91
Q1123
median153
Q3188
95-th percentile229.4
Maximum330
Range302
Interquartile range (IQR)65

Descriptive statistics

Standard deviation46.33355149
Coefficient of variation (CV)0.2959215413
Kurtosis0.7462843069
Mean156.5737705
Median Absolute Deviation (MAD)32
Skewness0.5165818941
Sum47755
Variance2146.797994
MonotocityNot monotonic
2020-09-17T09:53:42.770982image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
11170.2%
 
15850.1%
 
14050.1%
 
13350.1%
 
15450.1%
 
16450.1%
 
11340.1%
 
10140.1%
 
18940.1%
 
18840.1%
 
Other values (133)2576.4%
 
(Missing)369592.4%
 
ValueCountFrequency (%) 
281< 0.1%
 
401< 0.1%
 
591< 0.1%
 
681< 0.1%
 
731< 0.1%
 
ValueCountFrequency (%) 
3301< 0.1%
 
30720.1%
 
2971< 0.1%
 
2891< 0.1%
 
26320.1%
 

TroponinI_min
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct86
Distinct (%)42.0%
Missing3795
Missing (%)94.9%
Infinite0
Infinite (%)0.0%
Mean5.584390244
Minimum0.3
Maximum49.2
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:43.020041image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.3
Q10.6
median1.6
Q37.2
95-th percentile24.36
Maximum49.2
Range48.9
Interquartile range (IQR)6.6

Descriptive statistics

Standard deviation8.62483022
Coefficient of variation (CV)1.54445335
Kurtosis6.876584391
Mean5.584390244
Median Absolute Deviation (MAD)1.3
Skewness2.509858419
Sum1144.8
Variance74.38769632
MonotocityNot monotonic
2020-09-17T09:53:43.266000image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.3220.5%
 
0.4160.4%
 
0.5100.2%
 
0.7100.2%
 
0.690.2%
 
0.880.2%
 
260.1%
 
160.1%
 
1.250.1%
 
0.950.1%
 
Other values (76)1082.7%
 
(Missing)379594.9%
 
ValueCountFrequency (%) 
0.3220.5%
 
0.4160.4%
 
0.5100.2%
 
0.690.2%
 
0.7100.2%
 
ValueCountFrequency (%) 
49.21< 0.1%
 
42.91< 0.1%
 
40.11< 0.1%
 
36.71< 0.1%
 
33.41< 0.1%
 

TroponinI_max
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct112
Distinct (%)54.6%
Missing3795
Missing (%)94.9%
Infinite0
Infinite (%)0.0%
Mean8.427804878
Minimum0.3
Maximum49.2
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:43.509131image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.3
Q10.9
median2.7
Q312.3
95-th percentile38.06
Maximum49.2
Range48.9
Interquartile range (IQR)11.4

Descriptive statistics

Standard deviation11.49183673
Coefficient of variation (CV)1.363562267
Kurtosis2.909825184
Mean8.427804878
Median Absolute Deviation (MAD)2.3
Skewness1.878036632
Sum1727.7
Variance132.0623113
MonotocityNot monotonic
2020-09-17T09:53:43.765729image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.3120.3%
 
0.8100.2%
 
0.4100.2%
 
0.770.2%
 
2.660.1%
 
0.660.1%
 
250.1%
 
0.550.1%
 
1.250.1%
 
140.1%
 
Other values (102)1353.4%
 
(Missing)379594.9%
 
ValueCountFrequency (%) 
0.3120.3%
 
0.4100.2%
 
0.550.1%
 
0.660.1%
 
0.770.2%
 
ValueCountFrequency (%) 
49.21< 0.1%
 
48.31< 0.1%
 
47.51< 0.1%
 
44.21< 0.1%
 
43.41< 0.1%
 

TroponinI_med
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct122
Distinct (%)59.5%
Missing3795
Missing (%)94.9%
Infinite0
Infinite (%)0.0%
Mean6.937317073
Minimum0.3
Maximum49.2
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:44.014244image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.3
Q10.8
median2.1
Q39.55
95-th percentile28.52
Maximum49.2
Range48.9
Interquartile range (IQR)8.75

Descriptive statistics

Standard deviation9.647362492
Coefficient of variation (CV)1.390647478
Kurtosis4.058127928
Mean6.937317073
Median Absolute Deviation (MAD)1.7
Skewness2.046010284
Sum1422.15
Variance93.07160306
MonotocityNot monotonic
2020-09-17T09:53:44.271462image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.4120.3%
 
0.3120.3%
 
0.890.2%
 
0.570.2%
 
0.760.1%
 
0.950.1%
 
0.650.1%
 
1.840.1%
 
1.540.1%
 
1.140.1%
 
Other values (112)1373.4%
 
(Missing)379594.9%
 
ValueCountFrequency (%) 
0.3120.3%
 
0.4120.3%
 
0.4520.1%
 
0.570.2%
 
0.551< 0.1%
 
ValueCountFrequency (%) 
49.21< 0.1%
 
42.91< 0.1%
 
40.11< 0.1%
 
38.81< 0.1%
 
37.651< 0.1%
 

TroponinT_min
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct212
Distinct (%)24.6%
Missing3137
Missing (%)78.4%
Infinite0
Infinite (%)0.0%
Mean0.8142526072
Minimum0.01
Maximum24.04
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:44.511413image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.01
Q10.04
median0.11
Q30.49
95-th percentile4.173
Maximum24.04
Range24.03
Interquartile range (IQR)0.45

Descriptive statistics

Standard deviation2.194308154
Coefficient of variation (CV)2.694873967
Kurtosis41.63416667
Mean0.8142526072
Median Absolute Deviation (MAD)0.09
Skewness5.649018242
Sum702.7
Variance4.814988275
MonotocityNot monotonic
2020-09-17T09:53:44.753320image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.02802.0%
 
0.03751.9%
 
0.04571.4%
 
0.01521.3%
 
0.06350.9%
 
0.05300.8%
 
0.08280.7%
 
0.07280.7%
 
0.09250.6%
 
0.11230.6%
 
Other values (202)43010.8%
 
(Missing)313778.4%
 
ValueCountFrequency (%) 
0.01521.3%
 
0.02802.0%
 
0.03751.9%
 
0.04571.4%
 
0.05300.8%
 
ValueCountFrequency (%) 
24.041< 0.1%
 
22.511< 0.1%
 
201< 0.1%
 
17.881< 0.1%
 
14.111< 0.1%
 

TroponinT_max
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct266
Distinct (%)30.8%
Missing3137
Missing (%)78.4%
Infinite0
Infinite (%)0.0%
Mean1.333453071
Minimum0.01
Maximum24.91
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:44.998690image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.02
Q10.06
median0.17
Q30.94
95-th percentile7.109
Maximum24.91
Range24.9
Interquartile range (IQR)0.88

Descriptive statistics

Standard deviation3.259412412
Coefficient of variation (CV)2.4443398
Kurtosis22.6574609
Mean1.333453071
Median Absolute Deviation (MAD)0.14
Skewness4.391610914
Sum1150.77
Variance10.62376927
MonotocityNot monotonic
2020-09-17T09:53:45.398526image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.03581.5%
 
0.02581.5%
 
0.04380.9%
 
0.06330.8%
 
0.05280.7%
 
0.01270.7%
 
0.08260.7%
 
0.09240.6%
 
0.1230.6%
 
0.07220.5%
 
Other values (256)52613.2%
 
(Missing)313778.4%
 
ValueCountFrequency (%) 
0.01270.7%
 
0.02581.5%
 
0.03581.5%
 
0.04380.9%
 
0.05280.7%
 
ValueCountFrequency (%) 
24.911< 0.1%
 
24.81< 0.1%
 
24.461< 0.1%
 
24.041< 0.1%
 
23.931< 0.1%
 

TroponinT_med
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct308
Distinct (%)35.7%
Missing3137
Missing (%)78.4%
Infinite0
Infinite (%)0.0%
Mean1.053015064
Minimum0.01
Maximum24.04
Zeros0
Zeros (%)0.0%
Memory size31.2 KiB
2020-09-17T09:53:45.635810image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Quantile statistics

Minimum0.01
5-th percentile0.02
Q10.05
median0.14
Q30.775
95-th percentile5.5375
Maximum24.04
Range24.03
Interquartile range (IQR)0.725

Descriptive statistics

Standard deviation2.581096376
Coefficient of variation (CV)2.451148578
Kurtosis27.1536224
Mean1.053015064
Median Absolute Deviation (MAD)0.115
Skewness4.682379601
Sum908.752
Variance6.662058502
MonotocityNot monotonic
2020-09-17T09:53:45.880676image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%) 
0.02591.5%
 
0.03501.2%
 
0.04391.0%
 
0.01280.7%
 
0.06270.7%
 
0.07230.6%
 
0.09210.5%
 
0.05200.5%
 
0.08190.5%
 
0.13180.4%
 
Other values (298)55914.0%
 
(Missing)313778.4%
 
ValueCountFrequency (%) 
0.01280.7%
 
0.01580.2%
 
0.02591.5%
 
0.02590.2%
 
0.03501.2%
 
ValueCountFrequency (%) 
24.041< 0.1%
 
22.931< 0.1%
 
201< 0.1%
 
19.131< 0.1%
 
17.881< 0.1%
 

Correlations

2020-09-17T09:53:46.395232image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-09-17T09:53:53.217001image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-09-17T09:54:00.052613image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-09-17T09:54:06.978150image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2020-09-17T09:54:13.650839image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2020-09-17T09:52:01.263561image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T09:52:20.282885image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T09:52:28.645120image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/
2020-09-17T09:52:46.739238image/svg+xmlMatplotlib v3.3.1, https://matplotlib.org/

Sample

First rows

PATIENT_IDihdAgeGenderHeightICUTypeWeight_minWeight_maxWeight_medGCS_minGCS_maxGCS_medHR_minHR_maxHR_medNIDiasABP_minNIDiasABP_maxNIDiasABP_medNIMAP_minNIMAP_maxNIMAP_medNISysABP_minNISysABP_maxNISysABP_medRespRate_minRespRate_maxRespRate_medTemp_minTemp_maxTemp_medUrine_minUrine_maxUrine_medHCT_minHCT_maxHCT_medBUN_minBUN_maxBUN_medCreatinine_minCreatinine_maxCreatinine_medGlucose_minGlucose_maxGlucose_medHCO3_minHCO3_maxHCO3_medMg_minMg_maxMg_medPlatelets_minPlatelets_maxPlatelets_medK_minK_maxK_medNa_minNa_maxNa_medWBC_minWBC_maxWBC_medpH_minpH_maxpH_medPaCO2_minPaCO2_maxPaCO2_medPaO2_minPaO2_maxPaO2_medDiasABP_minDiasABP_maxDiasABP_medFiO2_minFiO2_maxFiO2_medMAP_minMAP_maxMAP_medMechVent_minMechVent_maxMechVent_medSysABP_minSysABP_maxSysABP_medSaO2_minSaO2_maxSaO2_medAlbumin_minAlbumin_maxAlbumin_medALP_minALP_maxALP_medALT_minALT_maxALT_medAST_minAST_maxAST_medBilirubin_minBilirubin_maxBilirubin_medLactate_minLactate_maxLactate_medCholesterol_minCholesterol_maxCholesterol_medTroponinI_minTroponinI_maxTroponinI_medTroponinT_minTroponinT_maxTroponinT_med
0132539054femaleNaNSurgical ICUNaNNaNNaN14.015.015.058.086.073.039.067.049.058.6792.3370.00096.0157.0110.012.024.018.035.138.237.7030.0900.0100.030.333.733.508.013.010.50.70.80.75115.0205.0160.026.028.027.01.51.91.70185.0221.0203.04.04.44.20136.0137.0136.59.411.210.30NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN000.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1132540076male175.3Cardiac Surgery Recovery Unit76.081.680.63.015.015.065.090.080.038.067.059.049.3388.3378.16572.0131.0115.0NaNNaNNaN34.537.937.450.0770.090.024.730.728.8016.021.018.00.81.31.20105.0146.0125.521.024.022.01.93.12.10135.0226.0181.03.54.33.90135.0139.0137.07.413.313.107.347.457.40033.046.040.082.0445.0153.032.081.059.00.41.00.543.0100.079.0010.066.0138.0116.593.099.097.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2132541044femaleNaNMedical ICU56.756.756.75.08.05.057.0113.085.566.095.079.083.33110.0097.670111.0150.0134.0NaNNaNNaN36.739.037.8518.0425.0100.026.729.428.703.08.03.00.30.40.30119.0143.0141.024.026.025.01.31.91.8072.0113.084.02.38.63.40137.0140.0138.03.76.24.207.477.517.50033.037.036.065.0232.0120.552.081.067.00.41.00.572.0107.090.0010.0103.0148.0125.095.095.095.02.32.72.5105.0127.0116.075.091.083.0164.0235.0199.52.83.02.90.91.91.30NaNNaNNaNNaNNaNNaNNaNNaNNaN
3132543068male180.3Medical ICU84.684.684.614.015.015.057.088.074.045.081.064.068.33100.7083.670102.0140.0120.06.024.016.035.137.036.40100.0700.0625.036.141.336.8010.023.020.00.70.90.70106.0129.0117.027.028.028.01.92.12.10284.0391.0315.03.84.24.00137.0141.0140.07.911.58.80NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN000.0NaNNaNNaNNaNNaNNaN4.44.44.4105.0105.0105.012.012.012.015.015.015.00.20.20.2NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
4132545088femaleNaNMedical ICUNaNNaNNaN15.015.015.065.094.073.026.096.042.052.33105.7075.330105.0157.0131.013.027.019.035.837.837.0016.0220.050.022.632.430.6025.045.035.01.01.01.0092.0113.0102.518.020.019.01.51.61.5597.0109.0103.03.86.03.90139.0140.0139.53.84.84.30NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN000.0NaNNaNNaNNaNNaNNaN3.33.33.3NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
5132547064male180.3Coronary Care Unit114.0114.0114.07.011.09.071.0101.091.052.089.070.561.67102.3081.98581.0129.0105.0NaNNaNNaN35.838.237.8030.01200.060.035.041.636.1515.019.016.50.71.40.90153.0264.0197.019.021.019.51.72.32.10173.0276.0197.03.45.13.90136.0141.0137.012.724.013.857.297.467.42031.045.035.078.0179.0101.055.087.075.00.40.50.562.0107.090.0010.073.0152.0120.096.098.097.0NaNNaNNaN101.0101.0101.045.060.052.547.0162.0104.50.40.40.4NaNNaNNaN212.0212.0212.01.31.31.30NaNNaNNaN
6132548068female162.6Medical ICU87.087.087.015.015.015.050.088.068.031.088.077.046.33121.00112.00077.0187.0178.010.024.012.035.837.136.7015.0190.050.030.032.931.9031.036.031.53.44.13.4594.0115.0105.523.026.025.01.91.91.90325.0337.0327.03.44.43.65138.0140.0139.06.26.76.20NaNNaNNaNNaNNaNNaNNaNNaNNaN56.0105.076.5NaNNaNNaN72.0174.0108.0000.0108.0218.0163.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN0.70.80.75NaNNaNNaN
7132551178female162.6Medical ICU48.448.448.48.015.010.055.0111.068.014.056.027.037.3392.6752.00076.0166.0101.0NaNNaNNaN35.638.037.0512.0130.040.032.634.133.0058.081.059.00.60.90.60116.0139.0123.011.018.013.01.64.12.2091.0102.096.03.94.74.50137.0141.0141.016.123.520.407.117.407.27024.042.031.086.0334.0105.027.054.039.50.41.00.545.089.066.0010.082.0172.0127.594.098.097.01.91.91.947.047.047.046.046.046.082.082.082.00.30.30.31.22.31.55NaNNaNNaN3.13.53.30NaNNaNNaN
8132554064femaleNaNMedical ICU60.760.760.715.015.015.0115.0137.0128.047.092.065.064.33104.0084.17093.0143.0125.519.044.035.536.737.336.80100.0300.0300.028.328.328.3021.023.022.00.70.70.70112.0113.0112.521.025.023.01.61.71.65696.0696.0696.03.94.54.20139.0139.0139.015.215.215.20NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN000.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
9132555074male175.3Cardiac Surgery Recovery Unit66.172.166.110.015.015.067.099.086.049.057.053.068.6782.6775.670108.0134.0121.0NaNNaNNaN34.838.236.7525.0360.080.026.431.529.2517.022.019.01.01.31.10106.0114.0110.023.026.025.01.81.81.80139.0157.0141.04.14.64.35139.0141.0140.09.011.89.707.297.417.34538.050.045.0102.0486.0159.543.076.059.00.41.00.561.0100.080.0010.078.0180.0123.096.099.097.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN

Last rows

PATIENT_IDihdAgeGenderHeightICUTypeWeight_minWeight_maxWeight_medGCS_minGCS_maxGCS_medHR_minHR_maxHR_medNIDiasABP_minNIDiasABP_maxNIDiasABP_medNIMAP_minNIMAP_maxNIMAP_medNISysABP_minNISysABP_maxNISysABP_medRespRate_minRespRate_maxRespRate_medTemp_minTemp_maxTemp_medUrine_minUrine_maxUrine_medHCT_minHCT_maxHCT_medBUN_minBUN_maxBUN_medCreatinine_minCreatinine_maxCreatinine_medGlucose_minGlucose_maxGlucose_medHCO3_minHCO3_maxHCO3_medMg_minMg_maxMg_medPlatelets_minPlatelets_maxPlatelets_medK_minK_maxK_medNa_minNa_maxNa_medWBC_minWBC_maxWBC_medpH_minpH_maxpH_medPaCO2_minPaCO2_maxPaCO2_medPaO2_minPaO2_maxPaO2_medDiasABP_minDiasABP_maxDiasABP_medFiO2_minFiO2_maxFiO2_medMAP_minMAP_maxMAP_medMechVent_minMechVent_maxMechVent_medSysABP_minSysABP_maxSysABP_medSaO2_minSaO2_maxSaO2_medAlbumin_minAlbumin_maxAlbumin_medALP_minALP_maxALP_medALT_minALT_maxALT_medAST_minAST_maxAST_medBilirubin_minBilirubin_maxBilirubin_medLactate_minLactate_maxLactate_medCholesterol_minCholesterol_maxCholesterol_medTroponinI_minTroponinI_maxTroponinI_medTroponinT_minTroponinT_maxTroponinT_med
3990142655043maleNaNMedical ICU92.993.092.97.015.011.093.0125.0107.045.052.052.059.6765.3365.33089.092.092.0NaNNaNNaN36.738.638.2010.0725.0110.035.642.939.2520.023.022.01.01.21.0095.0151.0118.022.025.025.01.52.12.00142.0217.0179.54.14.74.20142.0145.0144.015.219.717.457.287.437.34538.049.042.561.0157.092.061.094.073.00.41.00.570.0290.092.0010.087.0169.0125.596.098.097.0NaNNaNNaN48.050.049.0114.0150.0132.040.086.063.00.30.30.31.15.42.4NaNNaNNaNNaNNaNNaN0.020.020.02
3991142659088maleNaNCoronary Care Unit90.790.790.715.015.015.060.098.073.523.099.045.041.00107.3061.83572.0124.097.5NaNNaNNaN35.637.936.5020.0200.055.032.635.835.2029.039.034.01.41.61.50112.0123.0117.521.021.021.01.72.22.20109.0111.0110.03.04.33.65136.0139.0137.515.919.517.707.297.297.29040.040.040.080.080.080.0NaNNaNNaN1.01.01.0NaNNaNNaN010.0NaNNaNNaN95.095.095.0NaNNaNNaN163.0163.0163.0115.0115.0115.080.080.080.09.29.29.2NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
3992142661089male177.8Surgical ICU64.064.064.010.012.011.076.096.086.043.090.056.565.67113.0083.165105.0161.0134.012.025.020.036.537.536.9030.0600.090.032.436.332.5012.014.013.01.01.21.1094.0100.096.021.023.022.01.72.01.80289.0318.0303.53.84.24.20130.0136.0133.08.911.410.15NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN000.0NaNNaNNaNNaNNaNNaN3.63.63.6NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
3993142662086male162.6Medical ICU53.053.053.011.015.015.064.0117.079.013.091.040.045.67102.0068.00091.0154.0122.0NaNNaNNaN35.839.436.7014.0200.060.031.731.831.7557.069.063.02.22.22.20102.0106.0104.018.020.019.01.62.01.80160.0168.0164.04.34.74.50136.0137.0136.511.020.815.90NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN000.0NaNNaNNaNNaNNaNNaN2.62.62.6155.0192.0173.528.035.031.535.037.036.00.91.11.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
3994142664051femaleNaNSurgical ICU75.075.075.03.011.010.068.0119.0100.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN36.637.636.950.0640.080.029.030.129.109.015.010.00.40.50.50111.0128.0122.025.027.025.01.62.01.90107.0205.0135.03.73.93.90141.0143.0142.07.08.98.007.347.497.45036.050.039.084.0304.0105.552.091.068.00.40.50.464.0108.083.5010.096.0149.0113.098.098.098.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
3995142665070femaleNaNSurgical ICU87.087.087.03.015.014.083.0113.099.540.087.062.560.00104.0078.00088.0138.0109.5NaNNaNNaN35.537.837.1015.0150.040.026.931.629.0514.018.016.00.81.00.90106.0136.0121.021.022.021.51.02.01.70198.0218.0208.02.44.73.85136.0138.0136.011.517.514.507.357.427.38038.043.040.076.0205.0140.045.082.068.00.40.50.457.0112.086.5010.090.0154.0117.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN1.13.02.3NaNNaNNaNNaNNaNNaNNaNNaNNaN
3996142667025maleNaNMedical ICU166.4166.4166.415.015.015.059.0106.079.038.078.058.561.67100.7081.835109.0151.0125.514.022.018.035.537.436.70400.0900.0575.030.331.931.803.07.04.00.70.90.9065.0251.096.020.028.023.01.31.51.50118.0155.0118.03.44.13.70138.0142.0141.03.06.54.70NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN000.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN117.0117.0117.0NaNNaNNaNNaNNaNNaN
3997142670044maleNaNMedical ICU109.0109.0109.03.08.07.080.0143.094.540.090.060.560.33108.3078.83594.0156.0117.0NaNNaNNaN37.138.437.8010.0700.077.537.840.038.606.010.07.51.01.21.1597.0132.099.023.026.025.02.22.52.45220.0229.0226.03.64.03.80137.0142.0141.010.611.810.807.397.417.39038.044.044.0108.0212.0127.063.088.074.00.40.60.479.0103.089.5010.0113.0136.0127.5NaNNaNNaNNaNNaNNaN51.051.051.020.020.020.020.020.020.00.50.50.5NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
3998142671137maleNaNMedical ICU87.487.487.43.07.06.074.0101.088.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN37.439.438.702.040.06.027.129.228.8565.0114.089.07.611.79.65118.0140.0130.021.031.027.52.73.12.80288.0354.0330.54.15.04.75143.0145.0144.011.514.712.957.347.527.41038.051.044.091.0314.0169.059.0113.095.00.51.00.571.0143.0120.5010.098.0203.0173.0NaNNaNNaN3.13.13.1158.0176.0173.01513.02364.02036.01277.02038.01741.00.60.90.71.91.91.9NaNNaNNaNNaNNaNNaNNaNNaNNaN
3999142673078female157.5Surgical ICU70.788.888.83.015.010.059.0177.087.039.087.059.052.0092.0076.00091.0143.0122.0NaNNaNNaN34.637.936.3015.0500.035.022.338.834.4517.024.019.50.81.51.00126.0126.0126.018.024.019.02.12.62.4047.0148.0104.04.94.94.90141.0145.0144.04.811.910.207.037.427.31029.064.039.082.0305.0124.029.0153.054.50.51.00.531.0191.073.0010.038.0260.0108.095.099.098.02.22.22.242.046.044.09.028.018.599.0153.0126.00.50.70.61.07.72.6NaNNaNNaNNaNNaNNaNNaNNaNNaN